| | |
- builtins.object
-
- gen_ai_hub.document_grounding.clients.pipeline_api_client.PipelineAPIClient
- gen_ai_hub.document_grounding.clients.retrieval_api_client.RetrievalAPIClient
- gen_ai_hub.document_grounding.clients.vector_api_client.VectorAPIClient
- builtins.str(builtins.object)
-
- gen_ai_hub.document_grounding.models.pipeline.DocumentStatus(builtins.str, enum.Enum)
- gen_ai_hub.document_grounding.models.pipeline.PipelineExecutionStatus(builtins.str, enum.Enum)
- enum.Enum(builtins.object)
-
- gen_ai_hub.document_grounding.models.pipeline.DocumentStatus(builtins.str, enum.Enum)
- gen_ai_hub.document_grounding.models.pipeline.PipelineExecutionStatus(builtins.str, enum.Enum)
- pydantic.main.BaseModel(builtins.object)
-
- gen_ai_hub.document_grounding.models.pipeline.BasePipelineResponse
-
- gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineGetResponse
- gen_ai_hub.document_grounding.models.pipeline.S3PipelineGetResponse
- gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineGetResponse
- gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration
- gen_ai_hub.document_grounding.models.pipeline.DataRepositoryMetadataItem
- gen_ai_hub.document_grounding.models.pipeline.Document
- gen_ai_hub.document_grounding.models.pipeline.DocumentsStatusResponse
- gen_ai_hub.document_grounding.models.pipeline.GetPipelineExecutionsResponse
- gen_ai_hub.document_grounding.models.pipeline.GetPipelineStatusResponse
- gen_ai_hub.document_grounding.models.pipeline.GetPipelinesResponse
- gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfiguration
- gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfigurationGetResponse
- gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineCreateRequest
- gen_ai_hub.document_grounding.models.pipeline.ManualPipelineTrigger
- gen_ai_hub.document_grounding.models.pipeline.MetaData
- gen_ai_hub.document_grounding.models.pipeline.PipelineExecution
- gen_ai_hub.document_grounding.models.pipeline.PipelineIdResponse
- gen_ai_hub.document_grounding.models.pipeline.S3PipelineCreateRequest
- gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineCreateRequest
- gen_ai_hub.document_grounding.models.pipeline.SearchPipelineData
- gen_ai_hub.document_grounding.models.pipeline.SearchPipelineRequest
- gen_ai_hub.document_grounding.models.pipeline.SearchPipelinesResponse
- gen_ai_hub.document_grounding.models.pipeline.SharePointConfig
- gen_ai_hub.document_grounding.models.pipeline.SharePointSite
- gen_ai_hub.document_grounding.models.retrieval.DataRepositories
- gen_ai_hub.document_grounding.models.retrieval.DataRepository
- gen_ai_hub.document_grounding.models.retrieval.DataRepositoryWithDocuments
- gen_ai_hub.document_grounding.models.retrieval.RetrievalChunk
- gen_ai_hub.document_grounding.models.retrieval.RetrievalDataRepositorySearchResult
- gen_ai_hub.document_grounding.models.retrieval.RetrievalDocument
- gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair
-
- gen_ai_hub.document_grounding.models.retrieval.RetrievalDocumentKeyValueListPair
- gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResult
- gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultError
- gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultWithError
- gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchConfiguration
- gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchDocumentKeyValueListPair
- gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchFilter
- gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchInput
- gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchResults
- gen_ai_hub.document_grounding.models.vector.BaseDocument
-
- gen_ai_hub.document_grounding.models.vector.Document
- gen_ai_hub.document_grounding.models.vector.Collection
- gen_ai_hub.document_grounding.models.vector.CollectionCreateRequest
- gen_ai_hub.document_grounding.models.vector.CollectionCreatedResponse
- gen_ai_hub.document_grounding.models.vector.CollectionDeletedResponse
- gen_ai_hub.document_grounding.models.vector.CollectionPendingResponse
- gen_ai_hub.document_grounding.models.vector.CollectionsListResponse
- gen_ai_hub.document_grounding.models.vector.DocumentOutput
- gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks
- gen_ai_hub.document_grounding.models.vector.DocumentsChunk
- gen_ai_hub.document_grounding.models.vector.DocumentsCreateRequest
- gen_ai_hub.document_grounding.models.vector.DocumentsListResponse
- gen_ai_hub.document_grounding.models.vector.DocumentsResponse
- gen_ai_hub.document_grounding.models.vector.DocumentsUpdateRequest
- gen_ai_hub.document_grounding.models.vector.EmbeddingConfig
- gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk
- gen_ai_hub.document_grounding.models.vector.TextSearchRequest
- gen_ai_hub.document_grounding.models.vector.VectorChunk
- gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair
- gen_ai_hub.document_grounding.models.vector.VectorPerFilterSearchResult
- gen_ai_hub.document_grounding.models.vector.VectorSearchConfiguration
- gen_ai_hub.document_grounding.models.vector.VectorSearchDocumentKeyValueListPair
- gen_ai_hub.document_grounding.models.vector.VectorSearchFilter
- gen_ai_hub.document_grounding.models.vector.VectorSearchResults
class BaseDocument(pydantic.main.BaseModel) |
| |
BaseDocument(*, chunks: List[gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk], metadata: List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]) -> None
|
| |
- Method resolution order:
- BaseDocument
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'chunks': typing.List[gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk], 'metadata': typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.BaseDocument'>, 'config': {'title': 'BaseDocument'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche....document_grounding.models.vector.BaseDocument'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.BaseDocument:140540955901904', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'chunks': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk:140540954291216', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair:140540954456784', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'BaseDocument', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'chunks': FieldInfo(annotation=List[TextOnlyBaseChunk], required=True), 'metadata': FieldInfo(annotation=List[VectorKeyValueListPair], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="BaseDocument", validator=...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, chunks: List[gen_ai_hub.document_...g.models.vector.VectorKeyValueListPair]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'chunks': FieldInfo(annotation=List[TextOnlyBaseChunk], required=True), 'metadata': FieldInfo(annotation=List[VectorKeyValueListPair], required=True)}
|
class BasePipelineResponse(pydantic.main.BaseModel) |
| |
BasePipelineResponse(*, id: str, type: str, metadata: Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData] = None) -> None
|
| |
- Method resolution order:
- BasePipelineResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'id': <class 'str'>, 'metadata': typing.Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData], 'type': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.BasePipelineResponse'>, 'config': {'title': 'BasePipelineResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...grounding.models.pipeline.BasePipelineResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.BasePipelineResponse:140540955923024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'BasePipelineResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="BasePipelineResponse", va...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, type: str, metadata: Opt...unding.models.pipeline.MetaData] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=str, required=True)}
|
class Collection(pydantic.main.BaseModel) |
| |
Collection(*, id: str, title: Optional[str] = None, embeddingConfig: gen_ai_hub.document_grounding.models.vector.EmbeddingConfig, metadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]] = []) -> None
|
| |
- Method resolution order:
- Collection
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'embeddingConfig': <class 'gen_ai_hub.document_grounding.models.vector.EmbeddingConfig'>, 'id': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]], 'title': typing.Optional[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.Collection'>, 'config': {'title': 'Collection'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ub.document_grounding.models.vector.Collection'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.Collection:140540954289216', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'embeddingConfig': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.EmbeddingConfig'>, 'config': {'title': 'EmbeddingConfig'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.EmbeddingConfig:140540954457840', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'EmbeddingConfig', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': [], 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'title': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Collection', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'embeddingConfig': FieldInfo(annotation=EmbeddingConfig, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="Collection", validator=Mo...n",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, title: Optional[str] = N...ls.vector.VectorKeyValueListPair]] = []) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'embeddingConfig': FieldInfo(annotation=EmbeddingConfig, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
|
class CollectionCreateRequest(pydantic.main.BaseModel) |
| |
CollectionCreateRequest(*, title: Optional[str] = None, embeddingConfig: gen_ai_hub.document_grounding.models.vector.EmbeddingConfig, metadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]] = []) -> None
# --- Collection Models ---
|
| |
- Method resolution order:
- CollectionCreateRequest
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'embeddingConfig': <class 'gen_ai_hub.document_grounding.models.vector.EmbeddingConfig'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]], 'title': typing.Optional[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.CollectionCreateRequest'>, 'config': {'title': 'CollectionCreateRequest'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...rounding.models.vector.CollectionCreateRequest'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.CollectionCreateRequest:140540955903872', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'embeddingConfig': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.EmbeddingConfig'>, 'config': {'title': 'EmbeddingConfig'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.EmbeddingConfig:140540954457840', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'EmbeddingConfig', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': [], 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'title': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'CollectionCreateRequest', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'embeddingConfig': FieldInfo(annotation=EmbeddingConfig, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="CollectionCreateRequest",...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, title: Optional[str] = None, embe...ls.vector.VectorKeyValueListPair]] = []) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'embeddingConfig': FieldInfo(annotation=EmbeddingConfig, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
|
class CollectionCreatedResponse(pydantic.main.BaseModel) |
| |
CollectionCreatedResponse(*, collectionUrl: str, status: Literal['CREATED'] = 'CREATED') -> None
|
| |
- Method resolution order:
- CollectionCreatedResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'collectionURL': <class 'str'>, 'status': typing.Literal['CREATED']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.CollectionCreatedResponse'>, 'config': {'title': 'CollectionCreatedResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.vector.CollectionCreatedResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.CollectionCreatedResponse:140540954295264', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'collectionURL': {'metadata': {}, 'schema': {'type': 'str'}, 'serialization_alias': 'collectionUrl', 'type': 'model-field', 'validation_alias': 'collectionUrl'}, 'status': {'metadata': {}, 'schema': {'default': 'CREATED', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'CollectionCreatedResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'collectionURL': FieldInfo(annotation=str, required=True, alias='collectionUrl', alias_priority=2), 'status': FieldInfo(annotation=Literal['CREATED'], required=False, default='CREATED')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="CollectionCreatedResponse...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, collectionUrl: str, status: Literal['CREATED'] = 'CREATED') -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'collectionURL': FieldInfo(annotation=str, required=True, alias='collectionUrl', alias_priority=2), 'status': FieldInfo(annotation=Literal['CREATED'], required=False, default='CREATED')}
|
class CollectionDeletedResponse(pydantic.main.BaseModel) |
| |
CollectionDeletedResponse(*, collectionUrl: str, status: Literal['DELETED'] = 'DELETED') -> None
|
| |
- Method resolution order:
- CollectionDeletedResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'collectionURL': <class 'str'>, 'status': typing.Literal['DELETED']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.CollectionDeletedResponse'>, 'config': {'title': 'CollectionDeletedResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.vector.CollectionDeletedResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.CollectionDeletedResponse:140540954298288', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'collectionURL': {'metadata': {}, 'schema': {'type': 'str'}, 'serialization_alias': 'collectionUrl', 'type': 'model-field', 'validation_alias': 'collectionUrl'}, 'status': {'metadata': {}, 'schema': {'default': 'DELETED', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'CollectionDeletedResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'collectionURL': FieldInfo(annotation=str, required=True, alias='collectionUrl', alias_priority=2), 'status': FieldInfo(annotation=Literal['DELETED'], required=False, default='DELETED')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="CollectionDeletedResponse...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, collectionUrl: str, status: Literal['DELETED'] = 'DELETED') -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'collectionURL': FieldInfo(annotation=str, required=True, alias='collectionUrl', alias_priority=2), 'status': FieldInfo(annotation=Literal['DELETED'], required=False, default='DELETED')}
|
class CollectionPendingResponse(pydantic.main.BaseModel) |
| |
CollectionPendingResponse(*, location: str, status: Literal['PENDING'] = 'PENDING') -> None
|
| |
- Method resolution order:
- CollectionPendingResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'Location': <class 'str'>, 'status': typing.Literal['PENDING']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.CollectionPendingResponse'>, 'config': {'title': 'CollectionPendingResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.vector.CollectionPendingResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.CollectionPendingResponse:140540954299248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'Location': {'metadata': {}, 'schema': {'type': 'str'}, 'serialization_alias': 'location', 'type': 'model-field', 'validation_alias': 'location'}, 'status': {'metadata': {}, 'schema': {'default': 'PENDING', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'CollectionPendingResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'Location': FieldInfo(annotation=str, required=True, alias='location', alias_priority=2), 'status': FieldInfo(annotation=Literal['PENDING'], required=False, default='PENDING')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="CollectionPendingResponse...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, location: str, status: Literal['PENDING'] = 'PENDING') -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'Location': FieldInfo(annotation=str, required=True, alias='location', alias_priority=2), 'status': FieldInfo(annotation=Literal['PENDING'], required=False, default='PENDING')}
|
class CollectionsListResponse(pydantic.main.BaseModel) |
| |
CollectionsListResponse(*, count: Optional[int] = None, resources: List[gen_ai_hub.document_grounding.models.vector.Collection]) -> None
|
| |
- Method resolution order:
- CollectionsListResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'count': typing.Optional[int], 'resources': typing.List[gen_ai_hub.document_grounding.models.vector.Collection]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.CollectionsListResponse'>, 'config': {'title': 'CollectionsListResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...rounding.models.vector.CollectionsListResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.CollectionsListResponse:140540954290176', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'count': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'resources': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.Collection'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.Collection:140540954289216', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'CollectionsListResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'resources': FieldInfo(annotation=List[Collection], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="CollectionsListResponse",...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, count: Optional[int] = None, reso...ent_grounding.models.vector.Collection]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'resources': FieldInfo(annotation=List[Collection], required=True)}
|
class CommonConfiguration(pydantic.main.BaseModel) |
| |
CommonConfiguration(*, destination: str) -> None
|
| |
- Method resolution order:
- CommonConfiguration
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'destination': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'config': {'title': 'CommonConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche..._grounding.models.pipeline.CommonConfiguration'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration:140540955917024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'destination': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'CommonConfiguration', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'destination': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="CommonConfiguration", val...n",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, destination: str) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'destination': FieldInfo(annotation=str, required=True)}
|
class DataRepositories(pydantic.main.BaseModel) |
| |
DataRepositories(*, count: Optional[int] = None, resources: List[gen_ai_hub.document_grounding.models.retrieval.DataRepository]) -> None
|
| |
- Method resolution order:
- DataRepositories
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'count': typing.Optional[int], 'resources': typing.List[gen_ai_hub.document_grounding.models.retrieval.DataRepository]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.DataRepositories'>, 'config': {'title': 'DataRepositories'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...nt_grounding.models.retrieval.DataRepositories'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.DataRepositories:140540954459840', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'count': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'resources': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.DataRepository'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.DataRepository:140540954449776', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'DataRepositories', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'resources': FieldInfo(annotation=List[DataRepository], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DataRepositories", valida...s",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, count: Optional[int] = None, reso...unding.models.retrieval.DataRepository]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'resources': FieldInfo(annotation=List[DataRepository], required=True)}
|
class DataRepository(pydantic.main.BaseModel) |
| |
DataRepository(*, id: str, title: str, type: Union[Literal['vector', 'help.sap.com'], str], metadata: Optional[List[gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair]] = <factory>) -> None
|
| |
- Method resolution order:
- DataRepository
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'id': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_...ding.models.retrieval.RetrievalKeyValueListPair]], 'title': <class 'str'>, 'type': typing.Union[typing.Literal['vector', 'help.sap.com'], str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.DataRepository'>, 'config': {'title': 'DataRepository'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ment_grounding.models.retrieval.DataRepository'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.DataRepository:140540954449776', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'title': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'choices': [{...}, {...}], 'type': 'union'}, 'type': 'model-field'}}, 'model_name': 'DataRepository', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list), 'title': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Union[Literal['vector', 'help.sap.com'], str], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DataRepository", validato...y",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, title: str, type: Union[...RetrievalKeyValueListPair]] = <factory>) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list), 'title': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Union[Literal['vector', 'help.sap.com'], str], required=True)}
|
class DataRepositoryMetadataItem(pydantic.main.BaseModel) |
| |
DataRepositoryMetadataItem(*, key: str, value: List[str]) -> None
|
| |
- Method resolution order:
- DataRepositoryMetadataItem
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'key': <class 'str'>, 'value': typing.List[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.DataRepositoryMetadataItem'>, 'config': {'title': 'DataRepositoryMetadataItem'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ing.models.pipeline.DataRepositoryMetadataItem'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.DataRepositoryMetadataItem:140540954577776', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'DataRepositoryMetadataItem', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'key': FieldInfo(annotation=str, required=True), 'value': FieldInfo(annotation=List[str], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DataRepositoryMetadataIte...m",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, key: str, value: List[str]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'key': FieldInfo(annotation=str, required=True), 'value': FieldInfo(annotation=List[str], required=True)}
|
class DataRepositoryWithDocuments(pydantic.main.BaseModel) |
| |
DataRepositoryWithDocuments(*, id: str, title: str, metadata: Optional[List[gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair]] = <factory>, documents: List[gen_ai_hub.document_grounding.models.retrieval.RetrievalDocument]) -> None
|
| |
- Method resolution order:
- DataRepositoryWithDocuments
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'documents': typing.List[gen_ai_hub.document_grounding.models.retrieval.RetrievalDocument], 'id': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_...ding.models.retrieval.RetrievalKeyValueListPair]], 'title': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.DataRepositoryWithDocuments'>, 'config': {'title': 'DataRepositoryWithDocuments'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...g.models.retrieval.DataRepositoryWithDocuments'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.DataRepositoryWithDocuments:140540954451776', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'documents': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalDocument'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalDocument:140540954447728', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'title': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'DataRepositoryWithDocuments', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'documents': FieldInfo(annotation=List[RetrievalDocument], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list), 'title': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DataRepositoryWithDocumen...s",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, title: str, metadata: Op...ing.models.retrieval.RetrievalDocument]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'documents': FieldInfo(annotation=List[RetrievalDocument], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list), 'title': FieldInfo(annotation=str, required=True)}
|
class Document(pydantic.main.BaseModel) |
| |
Document(*, id: str, status: Optional[gen_ai_hub.document_grounding.models.pipeline.DocumentStatus] = None, viewLocation: Optional[str] = None, downloadLocation: Optional[str] = None, absoluteUrl: Optional[str] = None, title: Optional[str] = None, metadataId: Optional[str] = None, createdTimestamp: Optional[datetime.datetime] = None, lastUpdatedTimestamp: Optional[datetime.datetime] = None) -> None
|
| |
- Method resolution order:
- Document
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'absoluteUrl': typing.Optional[str], 'createdTimestamp': typing.Optional[datetime.datetime], 'downloadLocation': typing.Optional[str], 'id': <class 'str'>, 'lastUpdatedTimestamp': typing.Optional[datetime.datetime], 'metadataId': typing.Optional[str], 'status': typing.Optional[gen_ai_hub.document_grounding.models.pipeline.DocumentStatus], 'title': typing.Optional[str], 'viewLocation': typing.Optional[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.Document'>, 'config': {'title': 'Document'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ub.document_grounding.models.pipeline.Document'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.Document:140540954588880', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'absoluteUrl': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'createdTimestamp': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'downloadLocation': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'lastUpdatedTimestamp': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'metadataId': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'status': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'title': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'viewLocation': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Document', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'absoluteUrl': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'createdTimestamp': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'downloadLocation': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'id': FieldInfo(annotation=str, required=True), 'lastUpdatedTimestamp': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'metadataId': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'status': FieldInfo(annotation=Union[DocumentStatus, NoneType], required=False, default=None), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'viewLocation': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="Document", validator=Mode...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, status: Optional[gen_ai_...amp: Optional[datetime.datetime] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'absoluteUrl': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'createdTimestamp': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'downloadLocation': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'id': FieldInfo(annotation=str, required=True), 'lastUpdatedTimestamp': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'metadataId': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'status': FieldInfo(annotation=Union[DocumentStatus, NoneType], required=False, default=None), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'viewLocation': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
|
class DocumentOutput(pydantic.main.BaseModel) |
| |
DocumentOutput(*, id: str, metadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]] = [], chunks: List[gen_ai_hub.document_grounding.models.vector.VectorChunk]) -> None
|
| |
- Method resolution order:
- DocumentOutput
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'chunks': typing.List[gen_ai_hub.document_grounding.models.vector.VectorChunk], 'id': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentOutput'>, 'config': {'title': 'DocumentOutput'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ocument_grounding.models.vector.DocumentOutput'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentOutput:140540954310336', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'chunks': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorChunk'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorChunk:140540954309328', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': [], 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'DocumentOutput', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'chunks': FieldInfo(annotation=List[VectorChunk], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[])}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DocumentOutput", validato...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, metadata: Optional[List[...nt_grounding.models.vector.VectorChunk]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'chunks': FieldInfo(annotation=List[VectorChunk], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[])}
|
class DocumentStatus(builtins.str, enum.Enum) |
| |
DocumentStatus(value, names=None, *, module=None, qualname=None, type=None, start=1)
An enumeration.
|
| |
- Method resolution order:
- DocumentStatus
- builtins.str
- enum.Enum
- builtins.object
Data and other attributes defined here:
- DEINDEXED = <DocumentStatus.DEINDEXED: 'DEINDEXED'>
- FAILED = <DocumentStatus.FAILED: 'FAILED'>
- FAILED_TO_BE_RETRIED = <DocumentStatus.FAILED_TO_BE_RETRIED: 'FAILED_TO_BE_RETRIED'>
- INDEXED = <DocumentStatus.INDEXED: 'INDEXED'>
- REINDEXED = <DocumentStatus.REINDEXED: 'REINDEXED'>
- TO_BE_PROCESSED = <DocumentStatus.TO_BE_PROCESSED: 'TO_BE_PROCESSED'>
- TO_BE_SCHEDULED = <DocumentStatus.TO_BE_SCHEDULED: 'TO_BE_SCHEDULED'>
Data descriptors inherited from enum.Enum:
- name
- The name of the Enum member.
- value
- The value of the Enum member.
Readonly properties inherited from enum.EnumMeta:
- __members__
- Returns a mapping of member name->value.
This mapping lists all enum members, including aliases. Note that this
is a read-only view of the internal mapping.
|
class DocumentWithoutChunks(pydantic.main.BaseModel) |
| |
DocumentWithoutChunks(*, id: str, metadata: List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]) -> None
|
| |
- Method resolution order:
- DocumentWithoutChunks
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'id': <class 'str'>, 'metadata': typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks'>, 'config': {'title': 'DocumentWithoutChunks'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche..._grounding.models.vector.DocumentWithoutChunks'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks:140540955904928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair:140540954456784', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'DocumentWithoutChunks', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=List[VectorKeyValueListPair], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DocumentWithoutChunks", v...s",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, metadata: List[gen_ai_hu...g.models.vector.VectorKeyValueListPair]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=List[VectorKeyValueListPair], required=True)}
|
class DocumentsChunk(pydantic.main.BaseModel) |
| |
DocumentsChunk(*, id: str, title: str, metadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]] = [], documents: List[gen_ai_hub.document_grounding.models.vector.DocumentOutput]) -> None
|
| |
- Method resolution order:
- DocumentsChunk
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'documents': typing.List[gen_ai_hub.document_grounding.models.vector.DocumentOutput], 'id': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]], 'title': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentsChunk'>, 'config': {'title': 'DocumentsChunk'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ocument_grounding.models.vector.DocumentsChunk'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentsChunk:140540954311376', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'documents': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentOutput'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentOutput:140540954310336', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': [], 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'title': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'DocumentsChunk', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'documents': FieldInfo(annotation=List[DocumentOutput], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'title': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DocumentsChunk", validato...k",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, title: str, metadata: Op...grounding.models.vector.DocumentOutput]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'documents': FieldInfo(annotation=List[DocumentOutput], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'title': FieldInfo(annotation=str, required=True)}
|
class DocumentsCreateRequest(pydantic.main.BaseModel) |
| |
DocumentsCreateRequest(*, documents: List[gen_ai_hub.document_grounding.models.vector.BaseDocument]) -> None
|
| |
- Method resolution order:
- DocumentsCreateRequest
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'documents': typing.List[gen_ai_hub.document_grounding.models.vector.BaseDocument]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentsCreateRequest'>, 'config': {'title': 'DocumentsCreateRequest'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...grounding.models.vector.DocumentsCreateRequest'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentsCreateRequest:140540954446752', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'documents': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.BaseDocument'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.BaseDocument:140540955901904', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'DocumentsCreateRequest', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'documents': FieldInfo(annotation=List[BaseDocument], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DocumentsCreateRequest", ...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, documents: List[gen_ai_hub.document_grounding.models.vector.BaseDocument]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'documents': FieldInfo(annotation=List[BaseDocument], required=True)}
|
class DocumentsListResponse(pydantic.main.BaseModel) |
| |
DocumentsListResponse(*, documents: List[gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks]) -> None
|
| |
- Method resolution order:
- DocumentsListResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'documents': typing.List[gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentsListResponse'>, 'config': {'title': 'DocumentsListResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche..._grounding.models.vector.DocumentsListResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentsListResponse:140540954293232', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'documents': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks:140540955904928', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'DocumentsListResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'documents': FieldInfo(annotation=List[DocumentWithoutChunks], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DocumentsListResponse", v...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, documents: List[gen_ai_hub.docume...ng.models.vector.DocumentWithoutChunks]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'documents': FieldInfo(annotation=List[DocumentWithoutChunks], required=True)}
|
class DocumentsResponse(pydantic.main.BaseModel) |
| |
DocumentsResponse(*, count: Optional[int] = None, resources: List[gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks]) -> None
|
| |
- Method resolution order:
- DocumentsResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'count': typing.Optional[int], 'resources': typing.List[gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentsResponse'>, 'config': {'title': 'DocumentsResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ment_grounding.models.vector.DocumentsResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentsResponse:140540954294256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'count': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'resources': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentWithoutChunks:140540955904928', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'DocumentsResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'resources': FieldInfo(annotation=List[DocumentWithoutChunks], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DocumentsResponse", valid...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, count: Optional[int] = None, reso...ng.models.vector.DocumentWithoutChunks]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'resources': FieldInfo(annotation=List[DocumentWithoutChunks], required=True)}
|
class DocumentsStatusResponse(pydantic.main.BaseModel) |
| |
DocumentsStatusResponse(*, count: Optional[int], resources: List[gen_ai_hub.document_grounding.models.pipeline.Document]) -> None
|
| |
- Method resolution order:
- DocumentsStatusResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'count': typing.Optional[int], 'resources': typing.List[gen_ai_hub.document_grounding.models.pipeline.Document]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.DocumentsStatusResponse'>, 'config': {'title': 'DocumentsStatusResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.pipeline.DocumentsStatusResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.DocumentsStatusResponse:140540954590928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'count': {'metadata': {}, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'model-field'}, 'resources': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.Document'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.Document:140540954588880', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'DocumentsStatusResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'count': FieldInfo(annotation=Union[int, NoneType], required=True), 'resources': FieldInfo(annotation=List[Document], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DocumentsStatusResponse",...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, count: Optional[int], resources: ...ent_grounding.models.pipeline.Document]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'count': FieldInfo(annotation=Union[int, NoneType], required=True), 'resources': FieldInfo(annotation=List[Document], required=True)}
|
class DocumentsUpdateRequest(pydantic.main.BaseModel) |
| |
DocumentsUpdateRequest(*, documents: List[gen_ai_hub.document_grounding.models.vector.Document]) -> None
|
| |
- Method resolution order:
- DocumentsUpdateRequest
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'documents': typing.List[gen_ai_hub.document_grounding.models.vector.Document]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentsUpdateRequest'>, 'config': {'title': 'DocumentsUpdateRequest'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...grounding.models.vector.DocumentsUpdateRequest'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentsUpdateRequest:140540954292224', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'documents': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.Document'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.Document:140540955906928', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'DocumentsUpdateRequest', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'documents': FieldInfo(annotation=List[Document], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="DocumentsUpdateRequest", ...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, documents: List[gen_ai_hub.document_grounding.models.vector.Document]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'documents': FieldInfo(annotation=List[Document], required=True)}
|
class EmbeddingConfig(pydantic.main.BaseModel) |
| |
EmbeddingConfig(*, modelName: Optional[str] = 'text-embedding-3-large') -> None
# --- Embedding Config ---
|
| |
- Method resolution order:
- EmbeddingConfig
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'modelName': typing.Optional[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.EmbeddingConfig'>, 'config': {'title': 'EmbeddingConfig'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...cument_grounding.models.vector.EmbeddingConfig'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.EmbeddingConfig:140540954457840', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'modelName': {'metadata': {}, 'schema': {'default': 'text-embedding-3-large', 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'EmbeddingConfig', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'modelName': FieldInfo(annotation=Union[str, NoneType], required=False, default='text-embedding-3-large')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="EmbeddingConfig", validat...g",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, modelName: Optional[str] = 'text-embedding-3-large') -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'modelName': FieldInfo(annotation=Union[str, NoneType], required=False, default='text-embedding-3-large')}
|
class GetPipelineExecutionsResponse(pydantic.main.BaseModel) |
| |
GetPipelineExecutionsResponse(*, count: Optional[int], resources: List[gen_ai_hub.document_grounding.models.pipeline.PipelineExecution]) -> None
|
| |
- Method resolution order:
- GetPipelineExecutionsResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'count': typing.Optional[int], 'resources': typing.List[gen_ai_hub.document_grounding.models.pipeline.PipelineExecution]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.GetPipelineExecutionsResponse'>, 'config': {'title': 'GetPipelineExecutionsResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche....models.pipeline.GetPipelineExecutionsResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.GetPipelineExecutionsResponse:140540954586848', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'count': {'metadata': {}, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'model-field'}, 'resources': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.PipelineExecution'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.PipelineExecution:140540954584864', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'GetPipelineExecutionsResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'count': FieldInfo(annotation=Union[int, NoneType], required=True), 'resources': FieldInfo(annotation=List[PipelineExecution], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="GetPipelineExecutionsResp...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, count: Optional[int], resources: ...ding.models.pipeline.PipelineExecution]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'count': FieldInfo(annotation=Union[int, NoneType], required=True), 'resources': FieldInfo(annotation=List[PipelineExecution], required=True)}
|
class GetPipelineStatusResponse(pydantic.main.BaseModel) |
| |
GetPipelineStatusResponse(*, lastStarted: Optional[str], status: Optional[str]) -> None
|
| |
- Method resolution order:
- GetPipelineStatusResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'lastStarted': typing.Optional[str], 'status': typing.Optional[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.GetPipelineStatusResponse'>, 'config': {'title': 'GetPipelineStatusResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ding.models.pipeline.GetPipelineStatusResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.GetPipelineStatusResponse:140540954575760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'lastStarted': {'metadata': {}, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'model-field'}, 'status': {'metadata': {}, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'model-field'}}, 'model_name': 'GetPipelineStatusResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'lastStarted': FieldInfo(annotation=Union[str, NoneType], required=True), 'status': FieldInfo(annotation=Union[str, NoneType], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="GetPipelineStatusResponse...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, lastStarted: Optional[str], status: Optional[str]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'lastStarted': FieldInfo(annotation=Union[str, NoneType], required=True), 'status': FieldInfo(annotation=Union[str, NoneType], required=True)}
|
class GetPipelinesResponse(pydantic.main.BaseModel) |
| |
GetPipelinesResponse(*, count: Optional[int], resources: List[Annotated[gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineGetResponse | gen_ai_hub.document_grounding.models.pipeline.S3PipelineGetResponse | gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineGetResponse, FieldInfo(annotation=NoneType, required=True, discriminator='type')]]) -> None
|
| |
- Method resolution order:
- GetPipelinesResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'count': typing.Optional[int], 'resources': typing.List[typing.Annotated[gen_ai_hub.document...=NoneType, required=True, discriminator='type')]]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.GetPipelinesResponse'>, 'config': {'title': 'GetPipelinesResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...grounding.models.pipeline.GetPipelinesResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.GetPipelinesResponse:140540954571760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'count': {'metadata': {}, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'model-field'}, 'resources': {'metadata': {}, 'schema': {'items_schema': {'choices': {...}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'GetPipelinesResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'count': FieldInfo(annotation=Union[int, NoneType], required=True), 'resources': FieldInfo(annotation=List[Annotated[Union[MSShar...red=True, discriminator='type')]], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="GetPipelinesResponse", va...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, count: Optional[int], resources: ... required=True, discriminator='type')]]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'count': FieldInfo(annotation=Union[int, NoneType], required=True), 'resources': FieldInfo(annotation=List[Annotated[Union[MSShar...red=True, discriminator='type')]], required=True)}
|
class MSSharePointConfiguration(pydantic.main.BaseModel) |
| |
MSSharePointConfiguration(*, destination: str, sharePoint: gen_ai_hub.document_grounding.models.pipeline.SharePointConfig) -> None
|
| |
- Method resolution order:
- MSSharePointConfiguration
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'destination': <class 'str'>, 'sharePoint': <class 'gen_ai_hub.document_grounding.models.pipeline.SharePointConfig'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfiguration'>, 'config': {'title': 'MSSharePointConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ding.models.pipeline.MSSharePointConfiguration'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfiguration:140540955916016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'destination': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'sharePoint': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SharePointConfig'>, 'config': {'title': 'SharePointConfig'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SharePointConfig:140540955914992', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'SharePointConfig', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'MSSharePointConfiguration', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'destination': FieldInfo(annotation=str, required=True), 'sharePoint': FieldInfo(annotation=SharePointConfig, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="MSSharePointConfiguration...n",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, destination: str, sharePoint: gen...unding.models.pipeline.SharePointConfig) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'destination': FieldInfo(annotation=str, required=True), 'sharePoint': FieldInfo(annotation=SharePointConfig, required=True)}
|
class MSSharePointConfigurationGetResponse(pydantic.main.BaseModel) |
| |
MSSharePointConfigurationGetResponse(*, destination: str, sharePoint: gen_ai_hub.document_grounding.models.pipeline.SharePointConfig) -> None
|
| |
- Method resolution order:
- MSSharePointConfigurationGetResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'destination': <class 'str'>, 'sharePoint': <class 'gen_ai_hub.document_grounding.models.pipeline.SharePointConfig'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfigurationGetResponse'>, 'config': {'title': 'MSSharePointConfigurationGetResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche....pipeline.MSSharePointConfigurationGetResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfigurationGetResponse:140540954566704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'destination': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'sharePoint': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SharePointConfig'>, 'config': {'title': 'SharePointConfig'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SharePointConfig:140540955914992', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'SharePointConfig', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'MSSharePointConfigurationGetResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'destination': FieldInfo(annotation=str, required=True), 'sharePoint': FieldInfo(annotation=SharePointConfig, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="MSSharePointConfiguration...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, destination: str, sharePoint: gen...unding.models.pipeline.SharePointConfig) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'destination': FieldInfo(annotation=str, required=True), 'sharePoint': FieldInfo(annotation=SharePointConfig, required=True)}
|
class MSSharePointPipelineCreateRequest(pydantic.main.BaseModel) |
| |
MSSharePointPipelineCreateRequest(*, type: Literal['MSSharePoint'] = 'MSSharePoint', configuration: gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfiguration, metadata: Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData] = None) -> None
|
| |
- Method resolution order:
- MSSharePointPipelineCreateRequest
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'configuration': <class 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfiguration'>, 'metadata': typing.Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData], 'type': typing.Literal['MSSharePoint']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineCreateRequest'>, 'config': {'title': 'MSSharePointPipelineCreateRequest'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...els.pipeline.MSSharePointPipelineCreateRequest'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineCreateRequest:140540955918032', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'configuration': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfiguration'>, 'config': {'title': 'MSSharePointConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfiguration:140540955916016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'MSSharePointConfiguration', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'MSSharePoint', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MSSharePointPipelineCreateRequest', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'configuration': FieldInfo(annotation=MSSharePointConfiguration, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['MSSharePoint'], required=False, default='MSSharePoint')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="MSSharePointPipelineCreat...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, type: Literal['MSSharePoint'] = '...unding.models.pipeline.MetaData] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'configuration': FieldInfo(annotation=MSSharePointConfiguration, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['MSSharePoint'], required=False, default='MSSharePoint')}
|
class MSSharePointPipelineGetResponse(BasePipelineResponse) |
| |
MSSharePointPipelineGetResponse(*, id: str, type: Literal['MSSharePoint'] = 'MSSharePoint', metadata: Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData] = None, configuration: gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfigurationGetResponse) -> None
|
| |
- Method resolution order:
- MSSharePointPipelineGetResponse
- BasePipelineResponse
- pydantic.main.BaseModel
- builtins.object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'configuration': <class 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfigurationGetResponse'>, 'type': typing.Literal['MSSharePoint']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineGetResponse'>, 'config': {'title': 'MSSharePointPipelineGetResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...odels.pipeline.MSSharePointPipelineGetResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineGetResponse:140540954567744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'configuration': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfigurationGetResponse'>, 'config': {'title': 'MSSharePointConfigurationGetResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.MSSharePointConfigurationGetResponse:140540954566704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'MSSharePointConfigurationGetResponse', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'MSSharePoint', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MSSharePointPipelineGetResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'configuration': FieldInfo(annotation=MSSharePointConfigurationGetResponse, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['MSSharePoint'], required=False, default='MSSharePoint')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="MSSharePointPipelineGetRe...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, type: Literal['MSSharePo...ne.MSSharePointConfigurationGetResponse) -> None>
- model_config = {}
Data descriptors inherited from BasePipelineResponse:
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'configuration': FieldInfo(annotation=MSSharePointConfigurationGetResponse, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['MSSharePoint'], required=False, default='MSSharePoint')}
|
class ManualPipelineTrigger(pydantic.main.BaseModel) |
| |
ManualPipelineTrigger(*, pipelineId: str, metadataOnly: Optional[bool] = None) -> None
|
| |
- Method resolution order:
- ManualPipelineTrigger
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'metadataOnly': typing.Optional[bool], 'pipelineId': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.ManualPipelineTrigger'>, 'config': {'title': 'ManualPipelineTrigger'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...rounding.models.pipeline.ManualPipelineTrigger'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.ManualPipelineTrigger:140540954591936', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'metadataOnly': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pipelineId': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ManualPipelineTrigger', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'metadataOnly': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'pipelineId': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="ManualPipelineTrigger", v...r",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, pipelineId: str, metadataOnly: Optional[bool] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'metadataOnly': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'pipelineId': FieldInfo(annotation=str, required=True)}
|
class MetaData(pydantic.main.BaseModel) |
| |
MetaData(*, destination: str) -> None
|
| |
- Method resolution order:
- MetaData
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'destination': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.MetaData'>, 'config': {'title': 'MetaData'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ub.document_grounding.models.pipeline.MetaData'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.MetaData:140540955909952', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'destination': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'MetaData', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'destination': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="MetaData", validator=Mode...a",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, destination: str) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'destination': FieldInfo(annotation=str, required=True)}
|
class PipelineAPIClient(builtins.object) |
| |
PipelineAPIClient(proxy_client: Optional[gen_ai_hub.proxy.gen_ai_hub_proxy.client.GenAIHubProxyClient] = None)
The Pipelines API creates and manages vector stores based on documents from user data repositories:
S3, SFTP, and Microsoft SharePoint.
Each pipeline represents a configured end-to-end process including the following steps:
- Fetches documents from a supported data source
- Preprocesses and chunks the document content, and generates semantic embeddings.
Semantic embeddings are multidimensional representations of textual information.
- Stores semantic embeddings into the HANA Vector Store
The Pipeline API is compatible with the following data repositories:
- Microsoft SharePoint
- AWS S3
- SFTP
See https://api.sap.com/api/DOCUMENT_GROUNDING_API/resource/Pipelines
|
| |
Methods defined here:
- __init__(self, proxy_client: Optional[gen_ai_hub.proxy.gen_ai_hub_proxy.client.GenAIHubProxyClient] = None)
- Initializes the PipelineAPIClient
:param proxy_client: proxy client to use for requests, defaults to None
:type proxy_client: Optional[GenAIHubProxyClient], optional
- create_pipeline(self, pipeline_request: Union[gen_ai_hub.document_grounding.models.pipeline.MSSharePointPipelineCreateRequest, gen_ai_hub.document_grounding.models.pipeline.S3PipelineCreateRequest, gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineCreateRequest]) -> gen_ai_hub.document_grounding.models.pipeline.PipelineIdResponse
- Create a document vectorization pipeline
:param pipeline_request: The object containing the pipeline configuration.
:type pipeline_request: CreatePipelineRequest
:return: ID of the created pipeline
:rtype: PipelineIdResponse
- delete_pipeline_by_id(self, pipeline_id: str) -> requests.models.Response
- Delete a pipeline by pipeline id
:param pipeline_id: ID of the pipeline to delete
:type pipeline_id: str
:return: Response of the delete operation
:rtype: requests.Response
- get_execution_document_by_id(self, pipeline_id: str, execution_id: str, document_id: str) -> gen_ai_hub.document_grounding.models.pipeline.Document
- Get Document by ID for a Pipeline Execution
:return: Document for the Pipeline Execution
:rtype: Document
- get_execution_documents(self, pipeline_id: str, execution_id: str, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.pipeline.DocumentsStatusResponse
- Get Documents for a Pipeline Execution
:param pipeline_id: Pipeline ID
:type pipeline_id: str
:param execution_id: Execution ID
:type execution_id: str
:param top: the maximum number of documents to return, defaults to None
:type top: Optional[int], optional
:param skip: number of documents to skip, defaults to None
:type skip: Optional[int], optional
:param count: flag to include count of total documents, defaults to None
:type count: Optional[bool], optional
:return: Documents for the Pipeline Execution
:rtype: DocumentsStatusResponse
- get_pipeline_by_id(self, pipeline_id: str) -> gen_ai_hub.document_grounding.models.pipeline.BasePipelineResponse
- Get details of a pipeline by pipeline id.
:param pipeline_id: Pipeline ID
:type pipeline_id: str
:return: Details of the pipeline
:rtype: BasePipelineResponse
- get_pipeline_document_by_id(self, pipeline_id: str, document_id: str) -> gen_ai_hub.document_grounding.models.pipeline.Document
- Get Document by ID for a Pipeline
:param pipeline_id: Pipeline ID
:type pipeline_id: str
:param document_id: Document ID
:type document_id: str
:return: Document for the Pipeline
:rtype: Document
- get_pipeline_documents(self, pipeline_id: str, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.pipeline.DocumentsStatusResponse
- Get Documents for a Pipeline
:param pipeline_id: Pipeline ID
:type pipeline_id: str
:param top: the maximum number of documents to return, defaults to None
:type top: Optional[int], optional
:param skip: number of documents to skip, defaults to None
:type skip: Optional[int], optional
:param count: flag to include count of total documents, defaults to None
:type count: Optional[bool], optional
:return: Documents for the Pipeline
:rtype: DocumentsStatusResponse
- get_pipeline_execution_by_id(self, pipeline_id: str, execution_id: str) -> gen_ai_hub.document_grounding.models.pipeline.PipelineExecution
- Get Pipeline Execution by ID
:param pipeline_id: Pipeline ID
:type pipeline_id: str
:param execution_id: Execution ID
:type execution_id: str
:return: Pipeline Execution
:rtype: PipelineExecution
- get_pipeline_executions(self, pipeline_id: str, last_execution: Optional[bool] = None, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.pipeline.GetPipelineExecutionsResponse
- Get Pipeline Executions
:param pipeline_id: Pipeline ID
:type pipeline_id: str
:param last_execution: flag to get only the last execution, defaults to None
:type last_execution: Optional[bool], optional
:param top: number of executions to retrieve, defaults to None
:type top: Optional[int], optional
:param skip: number of executions to skip, defaults to None
:type skip: Optional[int], optional
:param count: flag to include count of total executions, defaults to None
:type count: Optional[bool], optional
:return: Pipeline Executions
:rtype: GetPipelineExecutionsResponse
- get_pipeline_status(self, pipeline_id: str) -> gen_ai_hub.document_grounding.models.pipeline.GetPipelineStatusResponse
- Get pipeline status by pipeline id
:param pipeline_id: Pipeline ID
:type pipeline_id: str
:return: Status of the pipeline
:rtype: GetPipelineStatusResponse
- get_pipelines(self, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.pipeline.GetPipelinesResponse
- Get all pipelines.
:return: Get all pipelines
:rtype: GetPipelinesResponse
- search_pipelines(self, body: gen_ai_hub.document_grounding.models.pipeline.SearchPipelineRequest) -> gen_ai_hub.document_grounding.models.pipeline.SearchPipelinesResponse
- Pipeline Search by Metadata
:param body: The search request object containing metadata filters.
:type body: SearchPipelineRequest
:return: Search results containing matching pipelines.
:rtype: SearchPipelinesResponse
- trigger_pipeline(self, request: gen_ai_hub.document_grounding.models.pipeline.ManualPipelineTrigger) -> requests.models.Response
- Trigger Pipeline Manually
:param request: The manual trigger request object.
:type request: ManualPipelineTrigger
:return: Response of the trigger operation
:rtype: requests.Response
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class PipelineExecution(pydantic.main.BaseModel) |
| |
PipelineExecution(*, id: str, status: Optional[gen_ai_hub.document_grounding.models.pipeline.PipelineExecutionStatus] = None, createdAt: Optional[datetime.datetime] = None, modifiedAt: Optional[datetime.datetime] = None) -> None
|
| |
- Method resolution order:
- PipelineExecution
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'createdAt': typing.Optional[datetime.datetime], 'id': <class 'str'>, 'modifiedAt': typing.Optional[datetime.datetime], 'status': typing.Optional[gen_ai_hub.document_grounding.models.pipeline.PipelineExecutionStatus]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.PipelineExecution'>, 'config': {'title': 'PipelineExecution'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...nt_grounding.models.pipeline.PipelineExecution'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.PipelineExecution:140540954584864', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'createdAt': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'modifiedAt': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'status': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'PipelineExecution', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'createdAt': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'id': FieldInfo(annotation=str, required=True), 'modifiedAt': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'status': FieldInfo(annotation=Union[PipelineExecutionStatus, NoneType], required=False, default=None)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="PipelineExecution", valid...n",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, status: Optional[gen_ai_...dAt: Optional[datetime.datetime] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'createdAt': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'id': FieldInfo(annotation=str, required=True), 'modifiedAt': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'status': FieldInfo(annotation=Union[PipelineExecutionStatus, NoneType], required=False, default=None)}
|
class PipelineExecutionStatus(builtins.str, enum.Enum) |
| |
PipelineExecutionStatus(value, names=None, *, module=None, qualname=None, type=None, start=1)
An enumeration.
|
| |
- Method resolution order:
- PipelineExecutionStatus
- builtins.str
- enum.Enum
- builtins.object
Data and other attributes defined here:
- FINISHED = <PipelineExecutionStatus.FINISHED: 'FINISHED'>
- FINISHED_WITH_ERRORS = <PipelineExecutionStatus.FINISHED_WITH_ERRORS: 'FINISHEDWITHERRORS'>
- INPROGRESS = <PipelineExecutionStatus.INPROGRESS: 'INPROGRESS'>
- NEW = <PipelineExecutionStatus.NEW: 'NEW'>
- TIMEOUT = <PipelineExecutionStatus.TIMEOUT: 'TIMEOUT'>
- UNKNOWN = <PipelineExecutionStatus.UNKNOWN: 'UNKNOWN'>
Data descriptors inherited from enum.Enum:
- name
- The name of the Enum member.
- value
- The value of the Enum member.
Readonly properties inherited from enum.EnumMeta:
- __members__
- Returns a mapping of member name->value.
This mapping lists all enum members, including aliases. Note that this
is a read-only view of the internal mapping.
|
class PipelineIdResponse(pydantic.main.BaseModel) |
| |
PipelineIdResponse(*, pipelineId: str) -> None
|
| |
- Method resolution order:
- PipelineIdResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'pipelineId': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.PipelineIdResponse'>, 'config': {'title': 'PipelineIdResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...t_grounding.models.pipeline.PipelineIdResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.PipelineIdResponse:140540955925088', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'pipelineId': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'PipelineIdResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'pipelineId': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="PipelineIdResponse", vali...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, pipelineId: str) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'pipelineId': FieldInfo(annotation=str, required=True)}
|
class RetrievalAPIClient(builtins.object) |
| |
RetrievalAPIClient(proxy_client: Optional[gen_ai_hub.proxy.gen_ai_hub_proxy.client.GenAIHubProxyClient] = None)
The Retrieval API enables querying and retrieving relevant content from configured data repositories,
such as vector or external document sources (e.g., help.sap.com).
Retrieval combines semantic search with repository metadata filtering and supports custom
retrieval configurations for chunk/document granularity.
Reference: https://api.sap.com/api/DOCUMENT_GROUNDING_API/resource/Retrieval
|
| |
Methods defined here:
- __init__(self, proxy_client: Optional[gen_ai_hub.proxy.gen_ai_hub_proxy.client.GenAIHubProxyClient] = None)
- Initialize the RetrievalAPIClient.
:param proxy_client: Optional proxy client for making API requests.
:type proxy_client: Optional[GenAIHubProxyClient], optional
- get_data_repositories(self, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.retrieval.DataRepositories
- List all data repositories available to the tenant.
:param top: the number of items to return, defaults to None
:type top: Optional[int], optional
:param skip: the number of items to skip, defaults to None
:type skip: Optional[int], optional
:param count: whether to include a count of total items, defaults to None
:type count: Optional[bool], optional
:return: DataRepositories model containing the list of data repositories
:rtype: DataRepositories
- get_data_repository_by_id(self, repository_id: str) -> gen_ai_hub.document_grounding.models.retrieval.DataRepository
- Get a single data repository by its unique ID.
:param repository_id: the unique identifier of the data repository
:type repository_id: str
:return: DataRepository model representing the data repository
:rtype: DataRepository
- search(self, search_input: gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchInput) -> gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchResults
- Perform a retrieval search for relevant content.
:param search_input: RetrievalSearchInput model defining the query and filters.
:type search_input: RetrievalSearchInput
:return: RetrievalSearchResults model containing repositories, documents, and chunks.
:rtype: RetrievalSearchResults
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class RetrievalChunk(pydantic.main.BaseModel) |
| |
RetrievalChunk(*, id: str, content: str, metadata: Optional[List[gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair]] = <factory>) -> None
|
| |
- Method resolution order:
- RetrievalChunk
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'content': <class 'str'>, 'id': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_...ding.models.retrieval.RetrievalKeyValueListPair]]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalChunk'>, 'config': {'title': 'RetrievalChunk'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ment_grounding.models.retrieval.RetrievalChunk'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalChunk:140540954597968', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'content': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'RetrievalChunk', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'content': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalChunk", validato...k",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, content: str, metadata: ...RetrievalKeyValueListPair]] = <factory>) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'content': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list)}
|
class RetrievalDataRepositorySearchResult(pydantic.main.BaseModel) |
| |
RetrievalDataRepositorySearchResult(*, dataRepository: gen_ai_hub.document_grounding.models.retrieval.DataRepositoryWithDocuments) -> None
|
| |
- Method resolution order:
- RetrievalDataRepositorySearchResult
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'dataRepository': <class 'gen_ai_hub.document_grounding.models.retrieval.DataRepositoryWithDocuments'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalDataRepositorySearchResult'>, 'config': {'title': 'RetrievalDataRepositorySearchResult'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche....retrieval.RetrievalDataRepositorySearchResult'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalDataRepositorySearchResult:140540954465888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'dataRepository': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.DataRepositoryWithDocuments'>, 'config': {'title': 'DataRepositoryWithDocuments'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.DataRepositoryWithDocuments:140540954451776', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'DataRepositoryWithDocuments', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'RetrievalDataRepositorySearchResult', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'dataRepository': FieldInfo(annotation=DataRepositoryWithDocuments, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalDataRepositorySe...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, dataRepository: gen_ai_hub.docume...s.retrieval.DataRepositoryWithDocuments) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'dataRepository': FieldInfo(annotation=DataRepositoryWithDocuments, required=True)}
|
class RetrievalDocument(pydantic.main.BaseModel) |
| |
RetrievalDocument(*, id: str, metadata: Optional[List[gen_ai_hub.document_grounding.models.retrieval.RetrievalDocumentKeyValueListPair]] = <factory>, chunks: List[gen_ai_hub.document_grounding.models.retrieval.RetrievalChunk]) -> None
|
| |
- Method resolution order:
- RetrievalDocument
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'chunks': typing.List[gen_ai_hub.document_grounding.models.retrieval.RetrievalChunk], 'id': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_...els.retrieval.RetrievalDocumentKeyValueListPair]]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalDocument'>, 'config': {'title': 'RetrievalDocument'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...t_grounding.models.retrieval.RetrievalDocument'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalDocument:140540954447728', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'chunks': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalChunk'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalChunk:140540954597968', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'RetrievalDocument', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'chunks': FieldInfo(annotation=List[RetrievalChunk], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[RetrievalDocumen... NoneType], required=False, default_factory=list)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalDocument", valid...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, metadata: Optional[List[...unding.models.retrieval.RetrievalChunk]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'chunks': FieldInfo(annotation=List[RetrievalChunk], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[RetrievalDocumen... NoneType], required=False, default_factory=list)}
|
class RetrievalDocumentKeyValueListPair(RetrievalKeyValueListPair) |
| |
RetrievalDocumentKeyValueListPair(*, key: str, value: List[str], matchMode: Optional[str]) -> None
|
| |
- Method resolution order:
- RetrievalDocumentKeyValueListPair
- RetrievalKeyValueListPair
- pydantic.main.BaseModel
- builtins.object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'matchMode': typing.Optional[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalDocumentKeyValueListPair'>, 'config': {'title': 'RetrievalDocumentKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ls.retrieval.RetrievalDocumentKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalDocumentKeyValueListPair:140540954594960', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'matchMode': {'metadata': {}, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'RetrievalDocumentKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'key': FieldInfo(annotation=str, required=True), 'matchMode': FieldInfo(annotation=Union[str, NoneType], required=True), 'value': FieldInfo(annotation=List[str], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalDocumentKeyValue...r",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, key: str, value: List[str], matchMode: Optional[str]) -> None>
- model_config = {}
Data descriptors inherited from RetrievalKeyValueListPair:
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'key': FieldInfo(annotation=str, required=True), 'matchMode': FieldInfo(annotation=Union[str, NoneType], required=True), 'value': FieldInfo(annotation=List[str], required=True)}
|
class RetrievalKeyValueListPair(pydantic.main.BaseModel) |
| |
RetrievalKeyValueListPair(*, key: str, value: List[str]) -> None
|
| |
- Method resolution order:
- RetrievalKeyValueListPair
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'key': <class 'str'>, 'value': typing.List[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair'>, 'config': {'title': 'RetrievalKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ing.models.retrieval.RetrievalKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair:140540954593920', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'RetrievalKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'key': FieldInfo(annotation=str, required=True), 'value': FieldInfo(annotation=List[str], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalKeyValueListPair...r",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, key: str, value: List[str]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'key': FieldInfo(annotation=str, required=True), 'value': FieldInfo(annotation=List[str], required=True)}
|
class RetrievalPerFilterSearchResult(pydantic.main.BaseModel) |
| |
RetrievalPerFilterSearchResult(*, filterId: str, results: List[gen_ai_hub.document_grounding.models.retrieval.RetrievalDataRepositorySearchResult] = <factory>) -> None
|
| |
- Method resolution order:
- RetrievalPerFilterSearchResult
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'filterId': <class 'str'>, 'results': typing.List[gen_ai_hub.document_grounding.models.retrieval.RetrievalDataRepositorySearchResult]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResult'>, 'config': {'title': 'RetrievalPerFilterSearchResult'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...odels.retrieval.RetrievalPerFilterSearchResult'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResult:140540954466896', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'filterId': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'results': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {'items_schema': {...}, 'type': 'list'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'RetrievalPerFilterSearchResult', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'filterId': FieldInfo(annotation=str, required=True), 'results': FieldInfo(annotation=List[RetrievalDataRepositor...rchResult], required=False, default_factory=list)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalPerFilterSearchR...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, filterId: str, results: List[gen_...DataRepositorySearchResult] = <factory>) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'filterId': FieldInfo(annotation=str, required=True), 'results': FieldInfo(annotation=List[RetrievalDataRepositor...rchResult], required=False, default_factory=list)}
|
class RetrievalPerFilterSearchResultError(pydantic.main.BaseModel) |
| |
RetrievalPerFilterSearchResultError(*, message: str) -> None
|
| |
- Method resolution order:
- RetrievalPerFilterSearchResultError
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'message': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultError'>, 'config': {'title': 'RetrievalPerFilterSearchResultError'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche....retrieval.RetrievalPerFilterSearchResultError'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultError:140540954448768', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'message': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'RetrievalPerFilterSearchResultError', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'message': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalPerFilterSearchR...r",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, message: str) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'message': FieldInfo(annotation=str, required=True)}
|
class RetrievalPerFilterSearchResultWithError(pydantic.main.BaseModel) |
| |
RetrievalPerFilterSearchResultWithError(*, filterId: str, error: gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultError) -> None
|
| |
- Method resolution order:
- RetrievalPerFilterSearchResultWithError
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'error': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultError'>, 'filterId': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultWithError'>, 'config': {'title': 'RetrievalPerFilterSearchResultWithError'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...rieval.RetrievalPerFilterSearchResultWithError'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.R...valPerFilterSearchResultWithError:140540954450752', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'error': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultError'>, 'config': {'title': 'RetrievalPerFilterSearchResultError'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultError:140540954448768', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'RetrievalPerFilterSearchResultError', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'filterId': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'RetrievalPerFilterSearchResultWithError', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'error': FieldInfo(annotation=RetrievalPerFilterSearchResultError, required=True), 'filterId': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalPerFilterSearchR...r",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, filterId: str, error: gen_ai_hub....val.RetrievalPerFilterSearchResultError) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'error': FieldInfo(annotation=RetrievalPerFilterSearchResultError, required=True), 'filterId': FieldInfo(annotation=str, required=True)}
|
class RetrievalSearchConfiguration(pydantic.main.BaseModel) |
| |
RetrievalSearchConfiguration(*, maxChunkCount: Optional[int] = None, maxDocumentCount: Optional[int] = None) -> None
|
| |
- Method resolution order:
- RetrievalSearchConfiguration
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'maxChunkCount': typing.Optional[int], 'maxDocumentCount': typing.Optional[int]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchConfiguration'>, 'config': {'title': 'RetrievalSearchConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche....models.retrieval.RetrievalSearchConfiguration'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchConfiguration:140540954453808', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'maxChunkCount': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'maxDocumentCount': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'RetrievalSearchConfiguration', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'maxChunkCount': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'maxDocumentCount': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalSearchConfigurat...n",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, maxChunkCount: Optional[int] = None, maxDocumentCount: Optional[int] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'maxChunkCount': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'maxDocumentCount': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}
|
class RetrievalSearchDocumentKeyValueListPair(pydantic.main.BaseModel) |
| |
RetrievalSearchDocumentKeyValueListPair(*, key: str, value: List[str], selectMode: Optional[List[str]] = None) -> None
|
| |
- Method resolution order:
- RetrievalSearchDocumentKeyValueListPair
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'key': <class 'str'>, 'selectMode': typing.Optional[typing.List[str]], 'value': typing.List[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchDocumentKeyValueListPair'>, 'config': {'title': 'RetrievalSearchDocumentKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...rieval.RetrievalSearchDocumentKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.R...valSearchDocumentKeyValueListPair:140540954595968', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'selectMode': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'RetrievalSearchDocumentKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'key': FieldInfo(annotation=str, required=True), 'selectMode': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'value': FieldInfo(annotation=List[str], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalSearchDocumentKe...r",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, key: str, value: List[str], selectMode: Optional[List[str]] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'key': FieldInfo(annotation=str, required=True), 'selectMode': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'value': FieldInfo(annotation=List[str], required=True)}
|
class RetrievalSearchFilter(pydantic.main.BaseModel) |
| |
RetrievalSearchFilter(*, id: str, dataRepositoryType: Union[Literal['vector', 'help.sap.com'], str], searchConfiguration: Optional[gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchConfiguration] = <factory>, dataRepositories: Optional[List[str]] = <factory>, dataRepositoryMetadata: Optional[List[gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair]] = <factory>, documentMetadata: Optional[List[gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchDocumentKeyValueListPair]] = <factory>, chunkMetadata: Optional[List[gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair]] = <factory>) -> None
|
| |
- Method resolution order:
- RetrievalSearchFilter
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'chunkMetadata': typing.Optional[typing.List[gen_ai_hub.document_...ding.models.retrieval.RetrievalKeyValueListPair]], 'dataRepositories': typing.Optional[typing.List[str]], 'dataRepositoryMetadata': typing.Optional[typing.List[gen_ai_hub.document_...ding.models.retrieval.RetrievalKeyValueListPair]], 'dataRepositoryType': typing.Union[typing.Literal['vector', 'help.sap.com'], str], 'documentMetadata': typing.Optional[typing.List[gen_ai_hub.document_...trieval.RetrievalSearchDocumentKeyValueListPair]], 'id': <class 'str'>, 'searchConfiguration': typing.Optional[gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchConfiguration]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'definitions': [{'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair'>, 'config': {'title': 'RetrievalKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ing.models.retrieval.RetrievalKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair:140540954593920', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {...}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {...}, 'type': 'model-field'}}, 'model_name': 'RetrievalKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchFilter'>, 'config': {'title': 'RetrievalSearchFilter'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ounding.models.retrieval.RetrievalSearchFilter'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchFilter:140540954455776', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'chunkMetadata': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'dataRepositories': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'dataRepositoryMetadata': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'dataRepositoryType': {'metadata': {}, 'schema': {'choices': [...], 'type': 'union'}, 'type': 'model-field'}, 'documentMetadata': {'metadata': {}, 'schema': {'default_factory': <class 'list'>, 'default_factory_takes_data': False, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'searchConfiguration': {'metadata': {}, 'schema': {'default_factory': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchConfiguration'>, 'default_factory_takes_data': False, 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'RetrievalSearchFilter', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'chunkMetadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list), 'dataRepositories': FieldInfo(annotation=Union[List[str], NoneType], required=False, default_factory=list), 'dataRepositoryMetadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list), 'dataRepositoryType': FieldInfo(annotation=Union[Literal['vector', 'help.sap.com'], str], required=True), 'documentMetadata': FieldInfo(annotation=Union[List[RetrievalSearchD... NoneType], required=False, default_factory=list), 'id': FieldInfo(annotation=str, required=True), 'searchConfiguration': FieldInfo(annotation=Union[RetrievalSearchConfig...se, default_factory=RetrievalSearchConfiguration)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...7fd23db21240) }), enabled_from_config: false })])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalSearchFilter", v...ator: Py(0x7fd23db211b0) })], cache_strings=True)
- __signature__ = <Signature (*, id: str, dataRepositoryType: Unio...RetrievalKeyValueListPair]] = <factory>) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'chunkMetadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list), 'dataRepositories': FieldInfo(annotation=Union[List[str], NoneType], required=False, default_factory=list), 'dataRepositoryMetadata': FieldInfo(annotation=Union[List[RetrievalKeyValu... NoneType], required=False, default_factory=list), 'dataRepositoryType': FieldInfo(annotation=Union[Literal['vector', 'help.sap.com'], str], required=True), 'documentMetadata': FieldInfo(annotation=Union[List[RetrievalSearchD... NoneType], required=False, default_factory=list), 'id': FieldInfo(annotation=str, required=True), 'searchConfiguration': FieldInfo(annotation=Union[RetrievalSearchConfig...se, default_factory=RetrievalSearchConfiguration)}
|
class RetrievalSearchInput(pydantic.main.BaseModel) |
| |
RetrievalSearchInput(*, query: str, filters: List[gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchFilter]) -> None
|
| |
- Method resolution order:
- RetrievalSearchInput
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'filters': typing.List[gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchFilter], 'query': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'definitions': [{'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair'>, 'config': {'title': 'RetrievalKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ing.models.retrieval.RetrievalKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair:140540954593920', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {...}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {...}, 'type': 'model-field'}}, 'model_name': 'RetrievalKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchInput'>, 'config': {'title': 'RetrievalSearchInput'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...rounding.models.retrieval.RetrievalSearchInput'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchInput:140540954463856', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'filters': {'metadata': {}, 'schema': {'items_schema': {...}, 'type': 'list'}, 'type': 'model-field'}, 'query': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'RetrievalSearchInput', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'filters': FieldInfo(annotation=List[RetrievalSearchFilter], required=True), 'query': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...7fd23db21240) }), enabled_from_config: false })])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalSearchInput", va...ator: Py(0x7fd23db211b0) })], cache_strings=True)
- __signature__ = <Signature (*, query: str, filters: List[gen_ai_...models.retrieval.RetrievalSearchFilter]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'filters': FieldInfo(annotation=List[RetrievalSearchFilter], required=True), 'query': FieldInfo(annotation=str, required=True)}
|
class RetrievalSearchResults(pydantic.main.BaseModel) |
| |
RetrievalSearchResults(*, results: List[Union[gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResult, gen_ai_hub.document_grounding.models.retrieval.RetrievalPerFilterSearchResultWithError]]) -> None
|
| |
- Method resolution order:
- RetrievalSearchResults
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'results': typing.List[typing.Union[gen_ai_hub.document_gro...trieval.RetrievalPerFilterSearchResultWithError]]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchResults'>, 'config': {'title': 'RetrievalSearchResults'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.retrieval.RetrievalSearchResults'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchResults:140540954452752', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'results': {'metadata': {}, 'schema': {'items_schema': {'choices': [...], 'type': 'union'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'RetrievalSearchResults', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'results': FieldInfo(annotation=List[Union[RetrievalPerFilt...lPerFilterSearchResultWithError]], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="RetrievalSearchResults", ...s",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, results: List[Union[gen_ai_hub.do...trievalPerFilterSearchResultWithError]]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'results': FieldInfo(annotation=List[Union[RetrievalPerFilt...lPerFilterSearchResultWithError]], required=True)}
|
class S3PipelineCreateRequest(pydantic.main.BaseModel) |
| |
S3PipelineCreateRequest(*, type: Literal['S3'] = 'S3', configuration: gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration, metadata: Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData] = None) -> None
|
| |
- Method resolution order:
- S3PipelineCreateRequest
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'configuration': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'metadata': typing.Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData], 'type': typing.Literal['S3']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.S3PipelineCreateRequest'>, 'config': {'title': 'S3PipelineCreateRequest'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.pipeline.S3PipelineCreateRequest'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.S3PipelineCreateRequest:140540955920032', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'configuration': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'config': {'title': 'CommonConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration:140540955917024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'CommonConfiguration', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'S3', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'S3PipelineCreateRequest', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'configuration': FieldInfo(annotation=CommonConfiguration, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['S3'], required=False, default='S3')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="S3PipelineCreateRequest",...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, type: Literal['S3'] = 'S3', confi...unding.models.pipeline.MetaData] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'configuration': FieldInfo(annotation=CommonConfiguration, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['S3'], required=False, default='S3')}
|
class S3PipelineGetResponse(BasePipelineResponse) |
| |
S3PipelineGetResponse(*, id: str, type: Literal['S3'] = 'S3', metadata: Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData] = None, configuration: gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration) -> None
|
| |
- Method resolution order:
- S3PipelineGetResponse
- BasePipelineResponse
- pydantic.main.BaseModel
- builtins.object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'configuration': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'type': typing.Literal['S3']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.S3PipelineGetResponse'>, 'config': {'title': 'S3PipelineGetResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...rounding.models.pipeline.S3PipelineGetResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.S3PipelineGetResponse:140540954568704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'configuration': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'config': {'title': 'CommonConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration:140540955917024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'CommonConfiguration', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'S3', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'S3PipelineGetResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'configuration': FieldInfo(annotation=CommonConfiguration, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['S3'], required=False, default='S3')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="S3PipelineGetResponse", v...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, type: Literal['S3'] = 'S...ing.models.pipeline.CommonConfiguration) -> None>
- model_config = {}
Data descriptors inherited from BasePipelineResponse:
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'configuration': FieldInfo(annotation=CommonConfiguration, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['S3'], required=False, default='S3')}
|
class SFTPPipelineCreateRequest(pydantic.main.BaseModel) |
| |
SFTPPipelineCreateRequest(*, type: Literal['SFTP'] = 'SFTP', configuration: gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration, metadata: Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData] = None) -> None
|
| |
- Method resolution order:
- SFTPPipelineCreateRequest
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'configuration': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'metadata': typing.Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData], 'type': typing.Literal['SFTP']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineCreateRequest'>, 'config': {'title': 'SFTPPipelineCreateRequest'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ding.models.pipeline.SFTPPipelineCreateRequest'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineCreateRequest:140540955922048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'configuration': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'config': {'title': 'CommonConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration:140540955917024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'CommonConfiguration', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'SFTP', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'SFTPPipelineCreateRequest', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'configuration': FieldInfo(annotation=CommonConfiguration, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['SFTP'], required=False, default='SFTP')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SFTPPipelineCreateRequest...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, type: Literal['SFTP'] = 'SFTP', c...unding.models.pipeline.MetaData] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'configuration': FieldInfo(annotation=CommonConfiguration, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['SFTP'], required=False, default='SFTP')}
|
class SFTPPipelineGetResponse(BasePipelineResponse) |
| |
SFTPPipelineGetResponse(*, id: str, type: Literal['SFTP'] = 'SFTP', metadata: Optional[gen_ai_hub.document_grounding.models.pipeline.MetaData] = None, configuration: gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration) -> None
|
| |
- Method resolution order:
- SFTPPipelineGetResponse
- BasePipelineResponse
- pydantic.main.BaseModel
- builtins.object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'configuration': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'type': typing.Literal['SFTP']}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineGetResponse'>, 'config': {'title': 'SFTPPipelineGetResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.pipeline.SFTPPipelineGetResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SFTPPipelineGetResponse:140540954569744', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'configuration': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration'>, 'config': {'title': 'CommonConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.CommonConfiguration:140540955917024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'CommonConfiguration', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'SFTP', 'schema': {'expected': [...], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'SFTPPipelineGetResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'configuration': FieldInfo(annotation=CommonConfiguration, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['SFTP'], required=False, default='SFTP')}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SFTPPipelineGetResponse",...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, type: Literal['SFTP'] = ...ing.models.pipeline.CommonConfiguration) -> None>
- model_config = {}
Data descriptors inherited from BasePipelineResponse:
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'configuration': FieldInfo(annotation=CommonConfiguration, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetaData, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Literal['SFTP'], required=False, default='SFTP')}
|
class SearchPipelineData(pydantic.main.BaseModel) |
| |
SearchPipelineData(*, pipelineId: str) -> None
|
| |
- Method resolution order:
- SearchPipelineData
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'pipelineId': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SearchPipelineData'>, 'config': {'title': 'SearchPipelineData'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...t_grounding.models.pipeline.SearchPipelineData'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SearchPipelineData:140540954579824', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'pipelineId': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'SearchPipelineData', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'pipelineId': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SearchPipelineData", vali...a",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, pipelineId: str) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'pipelineId': FieldInfo(annotation=str, required=True)}
|
class SearchPipelineRequest(pydantic.main.BaseModel) |
| |
SearchPipelineRequest(*, dataRepositoryMetadata: List[gen_ai_hub.document_grounding.models.pipeline.DataRepositoryMetadataItem]) -> None
|
| |
- Method resolution order:
- SearchPipelineRequest
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'dataRepositoryMetadata': typing.List[gen_ai_hub.document_grounding.models.pipeline.DataRepositoryMetadataItem]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SearchPipelineRequest'>, 'config': {'title': 'SearchPipelineRequest'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...rounding.models.pipeline.SearchPipelineRequest'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SearchPipelineRequest:140540954578784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'dataRepositoryMetadata': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.DataRepositoryMetadataItem'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.DataRepositoryMetadataItem:140540954577776', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'SearchPipelineRequest', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'dataRepositoryMetadata': FieldInfo(annotation=List[DataRepositoryMetadataItem], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SearchPipelineRequest", v...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, dataRepositoryMetadata: List[gen_...ls.pipeline.DataRepositoryMetadataItem]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'dataRepositoryMetadata': FieldInfo(annotation=List[DataRepositoryMetadataItem], required=True)}
|
class SearchPipelinesResponse(pydantic.main.BaseModel) |
| |
SearchPipelinesResponse(*, count: Optional[int], resources: List[gen_ai_hub.document_grounding.models.pipeline.SearchPipelineData]) -> None
|
| |
- Method resolution order:
- SearchPipelinesResponse
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'count': typing.Optional[int], 'resources': typing.List[gen_ai_hub.document_grounding.models.pipeline.SearchPipelineData]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SearchPipelinesResponse'>, 'config': {'title': 'SearchPipelinesResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.pipeline.SearchPipelinesResponse'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SearchPipelinesResponse:140540954580832', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'count': {'metadata': {}, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'model-field'}, 'resources': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SearchPipelineData'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SearchPipelineData:140540954579824', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'SearchPipelinesResponse', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'count': FieldInfo(annotation=Union[int, NoneType], required=True), 'resources': FieldInfo(annotation=List[SearchPipelineData], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SearchPipelinesResponse",...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, count: Optional[int], resources: ...ing.models.pipeline.SearchPipelineData]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'count': FieldInfo(annotation=Union[int, NoneType], required=True), 'resources': FieldInfo(annotation=List[SearchPipelineData], required=True)}
|
class SharePointConfig(pydantic.main.BaseModel) |
| |
SharePointConfig(*, site: gen_ai_hub.document_grounding.models.pipeline.SharePointSite) -> None
|
| |
- Method resolution order:
- SharePointConfig
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'site': <class 'gen_ai_hub.document_grounding.models.pipeline.SharePointSite'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SharePointConfig'>, 'config': {'title': 'SharePointConfig'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ent_grounding.models.pipeline.SharePointConfig'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SharePointConfig:140540955914992', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'site': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SharePointSite'>, 'config': {'title': 'SharePointSite'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [...]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SharePointSite:140540955911984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {...}, 'model_name': 'SharePointSite', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'SharePointConfig', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'site': FieldInfo(annotation=SharePointSite, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SharePointConfig", valida...g",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, site: gen_ai_hub.document_grounding.models.pipeline.SharePointSite) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'site': FieldInfo(annotation=SharePointSite, required=True)}
|
class SharePointSite(pydantic.main.BaseModel) |
| |
SharePointSite(*, name: str, includePaths: Optional[List[str]] = None) -> None
|
| |
- Method resolution order:
- SharePointSite
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'includePaths': typing.Optional[typing.List[str]], 'name': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.pipeline.SharePointSite'>, 'config': {'title': 'SharePointSite'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ument_grounding.models.pipeline.SharePointSite'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.pipeline.SharePointSite:140540955911984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'includePaths': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'name': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'SharePointSite', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'includePaths': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="SharePointSite", validato...e",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, name: str, includePaths: Optional[List[str]] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'includePaths': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True)}
|
class TextOnlyBaseChunk(pydantic.main.BaseModel) |
| |
TextOnlyBaseChunk(*, content: str, metadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]] = []) -> None
# --- Chunk and Document Models ---
|
| |
- Method resolution order:
- TextOnlyBaseChunk
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'content': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk'>, 'config': {'title': 'TextOnlyBaseChunk'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ment_grounding.models.vector.TextOnlyBaseChunk'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk:140540954291216', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'content': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': [], 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'TextOnlyBaseChunk', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'content': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[])}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="TextOnlyBaseChunk", valid...k",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, content: str, metadata: Optional[...ls.vector.VectorKeyValueListPair]] = []) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'content': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[])}
|
class TextSearchRequest(pydantic.main.BaseModel) |
| |
TextSearchRequest(*, query: str, filters: List[gen_ai_hub.document_grounding.models.vector.VectorSearchFilter]) -> None
|
| |
- Method resolution order:
- TextSearchRequest
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'filters': typing.List[gen_ai_hub.document_grounding.models.vector.VectorSearchFilter], 'query': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'definitions': [{'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair'>, 'config': {'title': 'VectorKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...grounding.models.vector.VectorKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair:140540954456784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {...}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {...}, 'type': 'model-field'}}, 'model_name': 'VectorKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.TextSearchRequest'>, 'config': {'title': 'TextSearchRequest'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ment_grounding.models.vector.TextSearchRequest'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.TextSearchRequest:140540954307360', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'filters': {'metadata': {}, 'schema': {'items_schema': {...}, 'type': 'list'}, 'type': 'model-field'}, 'query': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'TextSearchRequest', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'filters': FieldInfo(annotation=List[VectorSearchFilter], required=True), 'query': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...7fd23db23010) }), enabled_from_config: false })])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="TextSearchRequest", valid...ator: Py(0x7fd23db231c0) })], cache_strings=True)
- __signature__ = <Signature (*, query: str, filters: List[gen_ai_...nding.models.vector.VectorSearchFilter]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'filters': FieldInfo(annotation=List[VectorSearchFilter], required=True), 'query': FieldInfo(annotation=str, required=True)}
|
class VectorAPIClient(builtins.object) |
| |
VectorAPIClient(proxy_client: Optional[gen_ai_hub.proxy.gen_ai_hub_proxy.client.GenAIHubProxyClient] = None)
The Vector API provides management and search capabilities for vector-based document collections.
It enables creating, retrieving, updating, and deleting collections, as well as
managing documents and performing semantic vector searches within those collections.
Reference: https://api.sap.com/api/DOCUMENT_GROUNDING_API/resource/Vector
|
| |
Methods defined here:
- __init__(self, proxy_client: Optional[gen_ai_hub.proxy.gen_ai_hub_proxy.client.GenAIHubProxyClient] = None)
- Initializes the VectorAPIClient
:param proxy_client: Optional proxy client to use for requests
:type proxy_client: Optional[GenAIHubProxyClient], optional
- create_collection(self, collection_request: gen_ai_hub.document_grounding.models.vector.CollectionCreateRequest) -> requests.models.Response
- Create a new collection.
:param collection_request: The object containing the collection configuration.
:type collection_request: CollectionCreateRequest
:return: requests.Response empty object with 202 status code
:rtype: requests.Response
- create_documents(self, collection_id: str, request: gen_ai_hub.document_grounding.models.vector.DocumentsCreateRequest) -> gen_ai_hub.document_grounding.models.vector.DocumentsListResponse
- Create documents in a collection.
:param collection_id: The ID of the collection to add documents to.
:type collection_id: str
:param request: The object containing the documents to create.
:type request: DocumentsCreateRequest
:return: A DocumentsListResponse object containing the created documents
:rtype: DocumentsListResponse
- delete_collection(self, collection_id: str) -> requests.models.Response
- Delete collection by ID.
:param collection_id: The ID of the collection to delete.
:type collection_id: str
:return: requests.Response empty object with 204 status code
:rtype: requests.Response
- delete_document(self, collection_id: str, document_id: str) -> requests.models.Response
- Delete a document from a collection.
:param collection_id: The ID of the collection to delete the document from.
:type collection_id: str
:param document_id: The ID of the document to delete.
:type document_id: str
:return: requests.Response empty object with 204 status code
:rtype: requests.Response
- get_collection_by_id(self, collection_id: str) -> gen_ai_hub.document_grounding.models.vector.Collection
- Get collection details by ID.
:param collection_id: The ID of the collection to retrieve.
:type collection_id: str
:return: A Collection object containing the collection details
:rtype: Collection
- get_collection_creation_status(self, collection_id: str) -> typing.Annotated[gen_ai_hub.document_grounding.models.vector.CollectionCreatedResponse | gen_ai_hub.document_grounding.models.vector.CollectionPendingResponse, FieldInfo(annotation=NoneType, required=True, discriminator='status')]
- Get creation status for a collection.
:param collection_id: The ID of the collection to retrieve the creation status for.
:type collection_id: str
:return: A CollectionCreationStatusResponse object containing the creation status
:rtype: CollectionCreationStatusResponse
- get_collection_deletion_status(self, collection_id: str) -> typing.Annotated[gen_ai_hub.document_grounding.models.vector.CollectionDeletedResponse | gen_ai_hub.document_grounding.models.vector.CollectionPendingResponse, FieldInfo(annotation=NoneType, required=True, discriminator='status')]
- Get deletion status for a collection.
:param collection_id: The ID of the collection to retrieve the deletion status for.
:type collection_id: str
:return: A CollectionDeletionStatusResponse object containing the deletion status
:rtype: CollectionDeletionStatusResponse
- get_collections(self, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.vector.CollectionsListResponse
- Get all collections.
:param top: the number of collections to retrieve, defaults to None
:type top: Optional[int], optional
:param skip: the number of collections to skip, defaults to None
:type skip: Optional[int], optional
:param count: whether to include the total count of collections, defaults to None
:type count: Optional[bool], optional
:return: A CollectionsListResponse object containing the list of collections
:rtype: CollectionsListResponse
- get_document_by_id(self, collection_id: str, document_id: str) -> gen_ai_hub.document_grounding.models.vector.Document
- Get a document by ID from a collection.
:param collection_id: The ID of the collection to retrieve the document from.
:type collection_id: str
:param document_id: The ID of the document to retrieve.
:type document_id: str
:return: A Document object containing the document details
:rtype: Document
- get_documents(self, collection_id: str, top: Optional[int] = None, skip: Optional[int] = None, count: Optional[bool] = None) -> gen_ai_hub.document_grounding.models.vector.DocumentsResponse
- Get documents from a collection.
:param collection_id: The ID of the collection to retrieve documents from.
:type collection_id: str
:param top: the number of documents to retrieve, defaults to None
:type top: Optional[int], optional
:param skip: the number of documents to skip, defaults to None
:type skip: Optional[int], optional
:param count: whether to include the total count of documents, defaults to None
:type count: Optional[bool], optional
:return: A DocumentsResponse object containing the list of documents
:rtype: DocumentsResponse
- search(self, request: gen_ai_hub.document_grounding.models.vector.TextSearchRequest) -> gen_ai_hub.document_grounding.models.vector.VectorSearchResults
- Perform semantic search in vector collections.
:param request: The object containing the search parameters.
:type request: TextSearchRequest
:return: A VectorSearchResults object containing the search results
:rtype: VectorSearchResults
- update_documents(self, collection_id: str, request: gen_ai_hub.document_grounding.models.vector.DocumentsUpdateRequest) -> gen_ai_hub.document_grounding.models.vector.DocumentsListResponse
- Update documents in a collection.
:param collection_id: The ID of the collection to update documents in.
:type collection_id: str
:param request: The object containing the documents to update.
:type request: DocumentsUpdateRequest
:return: A DocumentsListResponse object containing the updated documents
:rtype: DocumentsListResponse
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class VectorChunk(pydantic.main.BaseModel) |
| |
VectorChunk(*, id: str, content: str, metadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]] = []) -> None
# --- Vector Search Results ---
|
| |
- Method resolution order:
- VectorChunk
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'content': <class 'str'>, 'id': <class 'str'>, 'metadata': typing.Optional[typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorChunk'>, 'config': {'title': 'VectorChunk'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...b.document_grounding.models.vector.VectorChunk'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorChunk:140540954309328', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'content': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'default': [], 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'VectorChunk', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'content': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[])}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="VectorChunk", validator=M...k",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, id: str, content: str, metadata: ...ls.vector.VectorKeyValueListPair]] = []) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'content': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[])}
|
VectorDocument = class Document(BaseDocument) |
| |
VectorDocument(*, chunks: List[gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk], metadata: List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair], id: str) -> None
|
| |
- Method resolution order:
- Document
- BaseDocument
- pydantic.main.BaseModel
- builtins.object
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'id': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.Document'>, 'config': {'title': 'Document'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche..._hub.document_grounding.models.vector.Document'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.Document:140540955906928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'chunks': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.TextOnlyBaseChunk:140540954291216', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'metadata': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair:140540954456784', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Document', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'chunks': FieldInfo(annotation=List[TextOnlyBaseChunk], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=List[VectorKeyValueListPair], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="Document", validator=Mode...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, chunks: List[gen_ai_hub.document_...vector.VectorKeyValueListPair], id: str) -> None>
- model_config = {}
Data descriptors inherited from BaseDocument:
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'chunks': FieldInfo(annotation=List[TextOnlyBaseChunk], required=True), 'id': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=List[VectorKeyValueListPair], required=True)}
|
class VectorKeyValueListPair(pydantic.main.BaseModel) |
| |
VectorKeyValueListPair(*, key: str, value: List[str]) -> None
# --- Common key-value metadata pair ---
|
| |
- Method resolution order:
- VectorKeyValueListPair
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'key': <class 'str'>, 'value': typing.List[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair'>, 'config': {'title': 'VectorKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...grounding.models.vector.VectorKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair:140540954456784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'VectorKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'key': FieldInfo(annotation=str, required=True), 'value': FieldInfo(annotation=List[str], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="VectorKeyValueListPair", ...r",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, key: str, value: List[str]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'key': FieldInfo(annotation=str, required=True), 'value': FieldInfo(annotation=List[str], required=True)}
|
class VectorPerFilterSearchResult(pydantic.main.BaseModel) |
| |
VectorPerFilterSearchResult(*, filterId: str, results: List[gen_ai_hub.document_grounding.models.vector.DocumentsChunk]) -> None
|
| |
- Method resolution order:
- VectorPerFilterSearchResult
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'filterId': <class 'str'>, 'results': typing.List[gen_ai_hub.document_grounding.models.vector.DocumentsChunk]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorPerFilterSearchResult'>, 'config': {'title': 'VectorPerFilterSearchResult'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ding.models.vector.VectorPerFilterSearchResult'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorPerFilterSearchResult:140540954312384', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'filterId': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'results': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.DocumentsChunk'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.DocumentsChunk:140540954311376', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'VectorPerFilterSearchResult', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'filterId': FieldInfo(annotation=str, required=True), 'results': FieldInfo(annotation=List[DocumentsChunk], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="VectorPerFilterSearchResu...t",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, filterId: str, results: List[gen_...grounding.models.vector.DocumentsChunk]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'filterId': FieldInfo(annotation=str, required=True), 'results': FieldInfo(annotation=List[DocumentsChunk], required=True)}
|
class VectorSearchConfiguration(pydantic.main.BaseModel) |
| |
VectorSearchConfiguration(*, maxChunkCount: Optional[int] = None, maxDocumentCount: Optional[int] = None) -> None
# --- Vector Search Models ---
|
| |
- Method resolution order:
- VectorSearchConfiguration
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'maxChunkCount': typing.Optional[int], 'maxDocumentCount': typing.Optional[int]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorSearchConfiguration'>, 'config': {'title': 'VectorSearchConfiguration'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...unding.models.vector.VectorSearchConfiguration'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorSearchConfiguration:140540954300272', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'maxChunkCount': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'maxDocumentCount': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'VectorSearchConfiguration', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'maxChunkCount': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'maxDocumentCount': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="VectorSearchConfiguration...n",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, maxChunkCount: Optional[int] = None, maxDocumentCount: Optional[int] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'maxChunkCount': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'maxDocumentCount': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}
|
class VectorSearchDocumentKeyValueListPair(pydantic.main.BaseModel) |
| |
VectorSearchDocumentKeyValueListPair(*, key: str, value: List[str], selectMode: Optional[List[str]] = None) -> None
|
| |
- Method resolution order:
- VectorSearchDocumentKeyValueListPair
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'key': <class 'str'>, 'selectMode': typing.Optional[typing.List[str]], 'value': typing.List[str]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorSearchDocumentKeyValueListPair'>, 'config': {'title': 'VectorSearchDocumentKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ls.vector.VectorSearchDocumentKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorSearchDocumentKeyValueListPair:140540954301280', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'selectMode': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {...}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'VectorSearchDocumentKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'key': FieldInfo(annotation=str, required=True), 'selectMode': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'value': FieldInfo(annotation=List[str], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="VectorSearchDocumentKeyVa...r",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, key: str, value: List[str], selectMode: Optional[List[str]] = None) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'key': FieldInfo(annotation=str, required=True), 'selectMode': FieldInfo(annotation=Union[List[str], NoneType], required=False, default=None), 'value': FieldInfo(annotation=List[str], required=True)}
|
class VectorSearchFilter(pydantic.main.BaseModel) |
| |
VectorSearchFilter(*, id: str, collectionIds: List[str], configuration: gen_ai_hub.document_grounding.models.vector.VectorSearchConfiguration, collectionMetadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]] = [], documentMetadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorSearchDocumentKeyValueListPair]] = [], chunkMetadata: Optional[List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]] = []) -> None
|
| |
- Method resolution order:
- VectorSearchFilter
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'chunkMetadata': typing.Optional[typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]], 'collectionIds': typing.List[str], 'collectionMetadata': typing.Optional[typing.List[gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair]], 'configuration': <class 'gen_ai_hub.document_grounding.models.vector.VectorSearchConfiguration'>, 'documentMetadata': typing.Optional[typing.List[gen_ai_hub.document_...els.vector.VectorSearchDocumentKeyValueListPair]], 'id': <class 'str'>}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'definitions': [{'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair'>, 'config': {'title': 'VectorKeyValueListPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...grounding.models.vector.VectorKeyValueListPair'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorKeyValueListPair:140540954456784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'key': {'metadata': {}, 'schema': {...}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {...}, 'type': 'model-field'}}, 'model_name': 'VectorKeyValueListPair', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorSearchFilter'>, 'config': {'title': 'VectorSearchFilter'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...ent_grounding.models.vector.VectorSearchFilter'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorSearchFilter:140540954303312', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'chunkMetadata': {'metadata': {}, 'schema': {'default': [], 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'collectionIds': {'metadata': {}, 'schema': {'items_schema': {...}, 'type': 'list'}, 'type': 'model-field'}, 'collectionMetadata': {'metadata': {}, 'schema': {'default': [], 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'configuration': {'metadata': {}, 'schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorSearchConfiguration'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorSearchConfiguration:140540954300272', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'model-field'}, 'documentMetadata': {'metadata': {}, 'schema': {'default': [], 'schema': {...}, 'type': 'default'}, 'type': 'model-field'}, 'id': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'VectorSearchFilter', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'chunkMetadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'collectionIds': FieldInfo(annotation=List[str], required=True), 'collectionMetadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'configuration': FieldInfo(annotation=VectorSearchConfiguration, required=True), 'documentMetadata': FieldInfo(annotation=Union[List[VectorSearchDocu...ListPair], NoneType], required=False, default=[]), 'id': FieldInfo(annotation=str, required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...7fd23db23010) }), enabled_from_config: false })])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="VectorSearchFilter", vali...ator: Py(0x7fd23db231c0) })], cache_strings=True)
- __signature__ = <Signature (*, id: str, collectionIds: List[str]...ls.vector.VectorKeyValueListPair]] = []) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'chunkMetadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'collectionIds': FieldInfo(annotation=List[str], required=True), 'collectionMetadata': FieldInfo(annotation=Union[List[VectorKeyValueListPair], NoneType], required=False, default=[]), 'configuration': FieldInfo(annotation=VectorSearchConfiguration, required=True), 'documentMetadata': FieldInfo(annotation=Union[List[VectorSearchDocu...ListPair], NoneType], required=False, default=[]), 'id': FieldInfo(annotation=str, required=True)}
|
class VectorSearchResults(pydantic.main.BaseModel) |
| |
VectorSearchResults(*, results: List[gen_ai_hub.document_grounding.models.vector.VectorPerFilterSearchResult]) -> None
|
| |
- Method resolution order:
- VectorSearchResults
- pydantic.main.BaseModel
- builtins.object
Data descriptors defined here:
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
- __annotations__ = {'results': typing.List[gen_ai_hub.document_grounding.models.vector.VectorPerFilterSearchResult]}
- __class_vars__ = set()
- __private_attributes__ = {}
- __pydantic_complete__ = True
- __pydantic_computed_fields__ = {}
- __pydantic_core_schema__ = {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorSearchResults'>, 'config': {'title': 'VectorSearchResults'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_sche...nt_grounding.models.vector.VectorSearchResults'>>]}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorSearchResults:140540954315424', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'results': {'metadata': {}, 'schema': {'items_schema': {'cls': <class 'gen_ai_hub.document_grounding.models.vector.VectorPerFilterSearchResult'>, 'config': {...}, 'custom_init': False, 'metadata': {...}, 'ref': 'gen_ai_hub.document_grounding.models.vector.VectorPerFilterSearchResult:140540954312384', 'root_model': False, 'schema': {...}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'VectorSearchResults', 'type': 'model-fields'}, 'type': 'model'}
- __pydantic_custom_init__ = False
- __pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={...zers={}, model_validators={}, computed_fields={})
- __pydantic_extra_info__ = None
- __pydantic_fields__ = {'results': FieldInfo(annotation=List[VectorPerFilterSearchResult], required=True)}
- __pydantic_generic_metadata__ = {'args': (), 'origin': None, 'parameters': ()}
- __pydantic_parent_namespace__ = None
- __pydantic_post_init__ = None
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoli...led_from_config: false,
},
), definitions=[])
- __pydantic_setattr_handlers__ = {}
- __pydantic_validator__ = SchemaValidator(title="VectorSearchResults", val...s",
},
), definitions=[], cache_strings=True)
- __signature__ = <Signature (*, results: List[gen_ai_hub.document...els.vector.VectorPerFilterSearchResult]) -> None>
- model_config = {}
Methods inherited from pydantic.main.BaseModel:
- __copy__(self) -> 'Self'
- Returns a shallow copy of the model.
- __deepcopy__(self, memo: 'dict[int, Any] | None' = None) -> 'Self'
- Returns a deep copy of the model.
- __delattr__(self, item: 'str') -> 'Any'
- Implement delattr(self, name).
- __eq__(self, other: 'Any') -> 'bool'
- Return self==value.
- __getattr__(self, item: 'str') -> 'Any'
- __getstate__(self) -> 'dict[Any, Any]'
- __init__(self, /, **data: 'Any') -> 'None'
- Create a new model by parsing and validating input data from keyword arguments.
Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
validated to form a valid model.
`self` is explicitly positional-only to allow `self` as a field name.
- __iter__(self) -> 'TupleGenerator'
- So `dict(model)` works.
- __pretty__(self, fmt: 'Callable[[Any], Any]', **kwargs: 'Any') -> 'Generator[Any]'
- Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.
- __replace__(self, **changes: 'Any') -> 'Self'
- # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by
# type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block:
- __repr__(self) -> 'str'
- Return repr(self).
- __repr_args__(self) -> '_repr.ReprArgs'
- __repr_name__(self) -> 'str'
- Name of the instance's class, used in __repr__.
- __repr_recursion__(self, object: 'Any') -> 'str'
- Returns the string representation of a recursive object.
- __repr_str__(self, join_str: 'str') -> 'str'
- __rich_repr__(self) -> 'RichReprResult'
- Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.
- __setattr__(self, name: 'str', value: 'Any') -> 'None'
- Implement setattr(self, name, value).
- __setstate__(self, state: 'dict[Any, Any]') -> 'None'
- __str__(self) -> 'str'
- Return str(self).
- copy(self, *, include: 'AbstractSetIntStr | MappingIntStrAny | None' = None, exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None, update: 'Dict[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```python {test="skip" lint="skip"}
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False) -> 'Dict[str, Any]'
- json(self, *, include: 'IncEx | None' = None, exclude: 'IncEx | None' = None, by_alias: 'bool' = False, exclude_unset: 'bool' = False, exclude_defaults: 'bool' = False, exclude_none: 'bool' = False, encoder: 'Callable[[Any], Any] | None' = PydanticUndefined, models_as_dict: 'bool' = PydanticUndefined, **dumps_kwargs: 'Any') -> 'str'
- model_copy(self, *, update: 'Mapping[str, Any] | None' = None, deep: 'bool' = False) -> 'Self'
- !!! abstract "Usage Documentation"
[`model_copy`](../concepts/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.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'dict[str, Any]'
- !!! abstract "Usage Documentation"
[`model_dump`](../concepts/serialization.md#python-mode)
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A dictionary representation of the model.
- 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, polymorphic_serialization: 'bool | None' = None) -> 'str'
- !!! abstract "Usage Documentation"
[`model_dump_json`](../concepts/serialization.md#json-mode)
Generates a JSON representation of the model using Pydantic's `to_json` method.
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.
polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
Returns:
A JSON string representation of the model.
- model_post_init(self, context: 'Any', /) -> 'None'
- Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
Class methods inherited from pydantic.main.BaseModel:
- __class_getitem__(typevar_values: 'type[Any] | tuple[type[Any], ...]') -> 'type[BaseModel] | _forward_ref.PydanticRecursiveRef' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_core_schema__(source: 'type[BaseModel]', handler: 'GetCoreSchemaHandler', /) -> 'CoreSchema' from pydantic._internal._model_construction.ModelMetaclass
- __get_pydantic_json_schema__(core_schema: 'CoreSchema', handler: 'GetJsonSchemaHandler', /) -> 'JsonSchemaValue' from pydantic._internal._model_construction.ModelMetaclass
- Hook into generating the model's JSON schema.
Args:
core_schema: A `pydantic-core` CoreSchema.
You can ignore this argument and call the handler with a new CoreSchema,
wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
or just call the handler with the original schema.
handler: Call into Pydantic's internal JSON schema generation.
This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
generation fails.
Since this gets called by `BaseModel.model_json_schema` you can override the
`schema_generator` argument to that function to change JSON schema generation globally
for a type.
Returns:
A JSON schema, as a Python object.
- __pydantic_init_subclass__(**kwargs: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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__() -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- 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].
- construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- from_orm(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- model_construct(_fields_set: 'set[str] | None' = None, **values: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
Otherwise, the field names from the `values` argument will be used.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
- model_json_schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: 'JsonSchemaMode' = 'validation', *, union_format: "Literal['any_of', 'primitive_type_array']" = 'any_of') -> 'dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_parametrized_name(params: 'tuple[type[Any], ...]') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_rebuild(*, force: 'bool' = False, raise_errors: 'bool' = True, _parent_namespace_depth: 'int' = 2, _types_namespace: 'MappingNamespace | None' = None) -> 'bool | None' from pydantic._internal._model_construction.ModelMetaclass
- Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
- model_validate(obj: 'Any', *, strict: 'bool | None' = None, extra: 'ExtraValues | None' = None, from_attributes: 'bool | None' = None, context: 'Any | None' = None, by_alias: 'bool | None' = None, by_name: 'bool | None' = None) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- model_validate_json(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' from pydantic._internal._model_construction.ModelMetaclass
- !!! 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.
- model_validate_strings(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' from pydantic._internal._model_construction.ModelMetaclass
- 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.
- parse_file(path: 'str | Path', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_obj(obj: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- parse_raw(b: 'str | bytes', *, content_type: 'str | None' = None, encoding: 'str' = 'utf8', proto: 'DeprecatedParseProtocol | None' = None, allow_pickle: 'bool' = False) -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
- schema(by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}') -> 'Dict[str, Any]' from pydantic._internal._model_construction.ModelMetaclass
- schema_json(*, by_alias: 'bool' = True, ref_template: 'str' = '#/$defs/{model}', **dumps_kwargs: 'Any') -> 'str' from pydantic._internal._model_construction.ModelMetaclass
- update_forward_refs(**localns: 'Any') -> 'None' from pydantic._internal._model_construction.ModelMetaclass
- validate(value: 'Any') -> 'Self' from pydantic._internal._model_construction.ModelMetaclass
Readonly properties inherited from pydantic.main.BaseModel:
- __fields_set__
- model_extra
- Get extra fields set during validation.
Returns:
A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
- model_fields_set
- Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
Data descriptors inherited from pydantic.main.BaseModel:
- __dict__
- dictionary for instance variables (if defined)
- __pydantic_extra__
- __pydantic_fields_set__
- __pydantic_private__
Data and other attributes inherited from pydantic.main.BaseModel:
- __hash__ = None
- __pydantic_root_model__ = False
- model_computed_fields = {}
- model_fields = {'results': FieldInfo(annotation=List[VectorPerFilterSearchResult], required=True)}
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