ResourceAdditionRequestChunkingConfiguration

class openapi_client.models.resource_addition_request_chunking_configuration.ResourceAdditionRequestChunkingConfiguration(**data)

Bases: BaseModel

Configuration for chunking strategy.

chunking_strategy: Optional[StrictStr]

max_tokens: Optional[StrictInt]

overlap_percentage: Optional[StrictInt]

max_token_layer2: Optional[StrictInt]

max_token_layer1: Optional[StrictInt]

overlap_tokens: Optional[StrictInt]

buffer_size: Optional[StrictInt]

breakpoint_percentile_threshold: Optional[StrictInt]

classmethod chunking_strategy_validate_enum(value)

Validates the enum

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True}

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

to_str()

Returns the string representation of the model using alias

Return type:

str

to_json()

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str)

Create an instance of ResourceAdditionRequestChunkingConfiguration from a JSON string

Return type:

Optional[Self]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj)

Create an instance of ResourceAdditionRequestChunkingConfiguration from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'breakpoint_percentile_threshold': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='BreakpointPercentileThreshold', alias_priority=2, description='The breakpoint percentile threshold to use for semantic chunking.'), 'buffer_size': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='BufferSize', alias_priority=2, description='The buffer size to use for semantic chunking.'), 'chunking_strategy': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='ChunkingStrategy', alias_priority=2, description='The chunking strategy to use.'), 'max_token_layer1': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='MaxTokenLayer1', alias_priority=2, description='The maximum number of tokens to use for hierarchical chunking layer 1.'), 'max_token_layer2': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='MaxTokenLayer2', alias_priority=2, description='The maximum number of tokens to use for hierarchical chunking layer 2.'), 'max_tokens': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='MaxTokens', alias_priority=2, description='The maximum number of tokens to use for fixed size chunking.'), 'overlap_percentage': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='OverlapPercentage', alias_priority=2, description='The overlap percentage to use for fixed size chunking.'), 'overlap_tokens': FieldInfo(annotation=Union[Annotated[int, Strict(strict=True)], NoneType], required=False, alias='OverlapTokens', alias_priority=2, description='The overlap tokens between the 2 layers used for hierarchical chunking.')}

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

This replaces Model.__fields__ from Pydantic V1.

model_post_init(__context)

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

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

Parameters:

• self (BaseModel) – The BaseModel instance.

• __context (Any) – The context.

Return type:

None