ExtraBody Class

Extra body for the Azure Chat Completion endpoint.

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.

Inheritance
ExtraBody

Constructor

ExtraBody(*, data_sources: list[Annotated[AzureAISearchDataSource | AzureCosmosDBDataSource, FieldInfo(annotation=NoneType, required=True, discriminator='type')]] | None = None, input_language: str | None = None, output_language: str | None = None)

Keyword-Only Parameters

Name Description
data_sources
Required
input_language
Required
output_language
Required

Attributes

model_computed_fields

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

model_config

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

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'populate_by_name': True, 'validate_assignment': True}

model_fields

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

This replaces Model.fields from Pydantic V1.

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data_sources': FieldInfo(annotation=Union[list[Annotated[Union[AzureAISearchDataSource, AzureCosmosDBDataSource], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], NoneType], required=False, default=None), 'input_language': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=2, serialization_alias='inputLanguage'), 'output_language': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, alias_priority=2, serialization_alias='outputLanguage')}

data_sources

data_sources: list[Annotated[AzureAISearchDataSource | AzureCosmosDBDataSource, FieldInfo(annotation=NoneType, required=True, discriminator='type')]] | None

input_language

input_language: str | None

output_language

output_language: str | None