model

superduperdb.ext.jina.model

Source code

JinaEmbedding

JinaEmbedding(self,
     identifier: str,
     db: dataclasses.InitVar[typing.Optional[ForwardRef('Datalayer')]] = None,
     uuid: str = <factory>,
     *,
     artifacts: 'dc.InitVar[t.Optional[t.Dict]]' = None,
     signature: str = 'singleton',
     datatype: 'EncoderArg' = None,
     output_schema: 't.Optional[Schema]' = None,
     flatten: 'bool' = False,
     model_update_kwargs: 't.Dict' = <factory>,
     predict_kwargs: 't.Dict' = <factory>,
     compute_kwargs: 't.Dict' = <factory>,
     validation: 't.Optional[Validation]' = None,
     metric_values: 't.Dict' = <factory>,
     model: 't.Optional[str]' = None,
     max_batch_size: 'int' = 8,
     api_key: Optional[str] = None,
     batch_size: int = 100,
     shape: Optional[Sequence[int]] = None) -> None

Parameter	Description
identifier	Identifier of the leaf.
db	Datalayer instance.
uuid	UUID of the leaf.
artifacts	A dictionary of artifacts paths and DataType objects
signature	Model signature.
datatype	DataType instance.
output_schema	Output schema (mapping of encoders).
flatten	Flatten the model outputs.
model_update_kwargs	The kwargs to use for model update.
predict_kwargs	Additional arguments to use at prediction time.
compute_kwargs	Kwargs used for compute backend job submit. Example (Ray backend): compute_kwargs = dict(resources=...).
validation	The validation Dataset instances to use.
metric_values	The metrics to evaluate on.
model	The Model to use, e.g. 'text-embedding-ada-002'
max_batch_size	Maximum batch size.
api_key	The API key to use for the predicto
batch_size	The batch size to use for the predictor.
shape	The shape of the embedding as tuple. If not provided, it will be obtained by sending a simple query to the API

Jina embedding predictor.

Jina

Jina(self,
     identifier: str,
     db: dataclasses.InitVar[typing.Optional[ForwardRef('Datalayer')]] = None,
     uuid: str = <factory>,
     *,
     artifacts: 'dc.InitVar[t.Optional[t.Dict]]' = None,
     signature: 'Signature' = '*args,
    **kwargs',
     datatype: 'EncoderArg' = None,
     output_schema: 't.Optional[Schema]' = None,
     flatten: 'bool' = False,
     model_update_kwargs: 't.Dict' = <factory>,
     predict_kwargs: 't.Dict' = <factory>,
     compute_kwargs: 't.Dict' = <factory>,
     validation: 't.Optional[Validation]' = None,
     metric_values: 't.Dict' = <factory>,
     model: 't.Optional[str]' = None,
     max_batch_size: 'int' = 8,
     api_key: Optional[str] = None) -> None

Parameter	Description
identifier	Identifier of the leaf.
db	Datalayer instance.
uuid	UUID of the leaf.
artifacts	A dictionary of artifacts paths and DataType objects
signature	Model signature.
datatype	DataType instance.
output_schema	Output schema (mapping of encoders).
flatten	Flatten the model outputs.
model_update_kwargs	The kwargs to use for model update.
predict_kwargs	Additional arguments to use at prediction time.
compute_kwargs	Kwargs used for compute backend job submit. Example (Ray backend): compute_kwargs = dict(resources=...).
validation	The validation Dataset instances to use.
metric_values	The metrics to evaluate on.
model	The Model to use, e.g. 'text-embedding-ada-002'
max_batch_size	Maximum batch size.
api_key	The API key to use for the predicto

Cohere predictor.