Helper Class

ExternalModelSchemas

class ExternalModelSchemas(input: List[Dict], output: List[Dict] = None)

External model schema

Variables:

• input (List[Dict]) – Model input data schema

• output (List[Dict]) – (optional) Model output data schema

ModelDetails

class ModelDetails(model_type: str = None, input_type: str = None, algorithm: str = None, label_type: str = None, label_column: str = None, prediction_type: str = None, software_spec: str = None, software_spec_id: str = None, provider: str = None)

External model model details

Variables:

• model_type (str) – Value for model type

• input_type (str) – Value for inout type

• algorithm (str) – Value for algorithm

• label_type (str) – Value for label type

• label_column (str) – Value for label column

• prediction_type (str) – Value for prediction type

• software_spec (str) – Value for software_spec

• software_spec_id (str) – Value for software_spec_id

• provider (str) – Value for provider,Available options are in Provider

TrainingDataReference

class TrainingDataReference(type: str = None, id: str = None, connection: Dict = None, location: Dict = None, schema: Dict = None)

Training data schema definition

Variables:

• type (str) – Type of training data reference. It has values as fs, url, data_asset or connection_asset.

• id (str) – ID of training data reference.

• connection (Dict) – (Optional) Connection details

• location (Dict) – (Optional) Location details

• schema (Dict) – Model training data schema

If type is fs, the parameters that user needs to input are - location.path If type is url, the parameters that user needs to provide are - training_data_references[].id and connection.url If type is data_asset, then - location.href If type is connection_asset, then location.file_name or location.table_name, connection.href

DeploymentDetails

class DeploymentDetails(identifier: str, name: str, deployment_type: str, scoring_endpoint: str = None, description: str = None)

External model deployment details

Variables:

• identifier (str) – Deployment identifier specific to providers.

• name (str) – Name of the deployment

• deployment_type (str) – Deployment type (i.e., online)

• scoring_endpoint (str) – Deployment scoring endpoint url.

• description (str) – (Optional) Description of the deployment

CloudPakforDataConfig

class CloudPakforDataConfig(service_url: str, username: str, password: str = None, api_key: str = None, disable_ssl_verification: bool = True, bedrock_url: str = None)

Configurations for Cloud Pak for Data environment

Variables:

• service_url (str) – Host URL of Cloud Pak for Data environment.

• username (str) – Environment username

• password (str) – Environment password

• api_key (str) – Environment api_key if IAM enabled

• disable_ssl_verification (bool) – Disable SSL verification. Default is True.

• bedrock_url (str) – (Optional) Foundational services (common-services-route ) url. User needs to get this url from CP4D admin. This url is required only when iam-integration is enabled on CP4D 4.0.x cluster.

ModelEntryProps

class ModelEntryProps(model_entry_catalog_id: str, asset_id: str = None, model_catalog_id: str = None, model_entry_id: str = None, model_entry_name: str = None, model_entry_desc: str = None, project_id: str = None, space_id: str = None, grc_model_id: str = None)

Model usecase Properties

Variables:

• model_entry_catalog_id (str) – Catalog ID where model usecase exist.

• asset_id (str) – Published model/asset ID. It is optional for external models ONLY.

• model_catalog_id (str) – (Optional) Catalog Id where model exist.

• model_entry_id (str) – (Optional) Existing Model usecase to link with.

• model_entry_name (str) – (Optional) New model usecase name. Used only when creating new model usecase.

• model_entry_description (str) – (Optional) New model usecase description. Used only when creating new model usecase.

• project_id (str) – (Optional) Project ID where the model exist.Not applicable for external models.

• space_id (str) – (Optional) Space ID where model exist Not applicable for external models.

• grc_model_id (str) – (Optional) Openpages model id. Only applicable for CPD environments.

PromptTemplate

class PromptTemplate(model_version: dict = None, prompt_variables: dict = None, prompt_instruction: str = None, input_prefix: str = None, output_prefix: str = None, structured_examples: dict = None, input: str = None, model_parameters: dict = None)

Details of a PromptTemplate

Variables:

• model_version (dict) – (Optional) Model version information and their respective values.

• prompt_variables (dict) – (Optional) Values for prompt variables in the format {prompt_variable: value}.

• prompt_instruction (str) – (Optional) Instruction for using the prompt.

• input_prefix (str) – (Optional) Prefix for input data.

• output_prefix (str) – (Optional)Prefix for output data.

• structured_examples (dict) – (Optional) Values of examples in the format {example_input: example_output}.

• input_prompt (str) – (Optional)Prompt input value.

• model_parameters (dict) – (Optional)Model parameters and their respective values.

DetachedPromptTemplate

Attention

• When specifying external information, ensure the following:

  • Provide both model_name and model_url together, or omit both.

  • The same rule applies for prompt_url and prompt_additional_info. Omit both if one is omitted

class DetachedPromptTemplate(prompt_id: str, model_id: str, model_provider: str, model_name: str = None, model_url: str = None, prompt_url: str = None, prompt_additional_info: list = None)

Details of a detached prompt template.

Variables:

• prompt_id (str) – ID of the external prompt.

• model_id (str) – ID of the external model associated with the prompt.

• model_provider (str) – Provider of the external model.

• model_name (str) – (Optional) Name of the external model (optional).

• model_url (str) – URL of the external model (optional).

• prompt_url (str) – URL of the external prompt (optional).

• prompt_additional_info (dict) – Additional information related to the external prompt (optional).