model

superduperdb.ext.openai.model

`OpenAIChatCompletion`

OpenAIChatCompletion(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,
     openai_api_key: Optional[str] = None,
     openai_api_base: Optional[str] = None,
     client_kwargs: Optional[dict] = <factory>,
     batch_size: int = 1,
     prompt: str = '') -> 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.
openai_api_key	The OpenAI API key.
openai_api_base	The server to use for requests.
client_kwargs	The kwargs to be passed to OpenAI
batch_size	The batch size to use.
prompt	The prompt to use to seed the response.

OpenAI chat completion predictor.

`OpenAIEmbedding`

OpenAIEmbedding(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,
     openai_api_key: Optional[str] = None,
     openai_api_base: Optional[str] = None,
     client_kwargs: Optional[dict] = <factory>,
     shape: Optional[Sequence[int]] = None,
     batch_size: int = 100) -> 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.
openai_api_key	The OpenAI API key.
openai_api_base	The server to use for requests.
client_kwargs	The kwargs to be passed to OpenAI
shape	The shape as `tuple` of the embedding.
batch_size	The batch size to use.

OpenAI embedding predictor.

`OpenAIAudioTranscription`

OpenAIAudioTranscription(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,
     openai_api_key: Optional[str] = None,
     openai_api_base: Optional[str] = None,
     client_kwargs: Optional[dict] = <factory>,
     takes_context: bool = True,
     prompt: str = '') -> 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.
openai_api_key	The OpenAI API key.
openai_api_base	The server to use for requests.
client_kwargs	The kwargs to be passed to OpenAI
takes_context	Whether the model takes context into account.
prompt	The prompt to guide the model's style.

OpenAI audio transcription predictor.

The prompt should contain the "context" format variable.

`OpenAIAudioTranslation`

OpenAIAudioTranslation(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,
     openai_api_key: Optional[str] = None,
     openai_api_base: Optional[str] = None,
     client_kwargs: Optional[dict] = <factory>,
     takes_context: bool = True,
     prompt: str = '',
     batch_size: int = 1) -> 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.
openai_api_key	The OpenAI API key.
openai_api_base	The server to use for requests.
client_kwargs	The kwargs to be passed to OpenAI
takes_context	Whether the model takes context into account.
prompt	The prompt to guide the model's style.
batch_size	The batch size to use.

OpenAI audio translation predictor.

The prompt should contain the "context" format variable.

`OpenAIImageCreation`

OpenAIImageCreation(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,
     openai_api_key: Optional[str] = None,
     openai_api_base: Optional[str] = None,
     client_kwargs: Optional[dict] = <factory>,
     takes_context: bool = True,
     prompt: str = '',
     n: int = 1,
     response_format: str = 'b64_json') -> 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.
openai_api_key	The OpenAI API key.
openai_api_base	The server to use for requests.
client_kwargs	The kwargs to be passed to OpenAI
takes_context	Whether the model takes context into account.
prompt	The prompt to use to seed the response.
n	The number of images to generate.
response_format	The response format to use.

OpenAI image creation predictor.

`OpenAIImageEdit`

OpenAIImageEdit(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,
     openai_api_key: Optional[str] = None,
     openai_api_base: Optional[str] = None,
     client_kwargs: Optional[dict] = <factory>,
     takes_context: bool = True,
     prompt: str = '',
     response_format: str = 'b64_json',
     n: int = 1) -> 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.
openai_api_key	The OpenAI API key.
openai_api_base	The server to use for requests.
client_kwargs	The kwargs to be passed to OpenAI
takes_context	Whether the model takes context into account.
prompt	The prompt to use to seed the response.
response_format	The response format to use.
n	The number of images to generate.

OpenAI image edit predictor.

OpenAIChatCompletion​

OpenAIEmbedding​

OpenAIAudioTranscription​

OpenAIAudioTranslation​

OpenAIImageCreation​

OpenAIImageEdit​