Python client changelog¶

Reference the changes introduced to new versions of DataRobot's Python client.

Python client v3.3¶

New Features¶

• Added support for Python 3.11.
• Added new a library, "strenum", to add StrEnum support while maintaining backwards compatibility with Python 3.7-3.10. DataRobot does not use the native StrEnum class in Python 3.11.
• Added a new class PredictionEnvironment for interacting with DataRobot prediction environments.
• Extended the advanced options available when setting a target to include new parameters: modelGroupId, modelRegimeId, and modelBaselines (part of the AdvancedOptions object). These parameters allow you to specify the user columns required to run time series models without feature derivation in OTV projects.

• Added a new method PredictionExplanations.create_on_training_data, for computing prediction explanation on training data.

• Added a new class RegisteredModel for interacting with DataRobot registered models to support the following methods:

• RegisteredModel.get to retrieve a RegisteredModel object by ID.
• RegisteredModel.list to list all registered models.
• RegisteredModel.archive to permanently archive registered model.
• RegisteredModel.update to update registered model.
• RegisteredModel.get_shared_roles to retrieve access control information for registered model.
• RegisteredModel.share to share a registered model.
• RegisteredModel.get_version to retrieve a RegisteredModelVersion object by ID.
• RegisteredModel.list_versions to list registered model versions.

• RegisteredModel.list_associated_deployments to list deployments associated with a registered model.

• Added a new class RegisteredModelVersion for interacting with DataRobot registered model versions (also known as model packages) to support the following methods:

• RegisteredModelVersion.create_for_external to create a new registered model version from an external model.
• RegisteredModelVersion.list_associated_deployments to list deployments associated with a registered model version.
• RegisteredModelVersion.create_for_leaderboard_item to create a new registered model version from a Leaderboard model.

• RegisteredModelVersion.create_for_custom_model_version to create a new registered model version from a custom model version.

• Added a new method Deployment.create_from_registered_model_version to support creating deployments from a registered model version.

• Added a new method Deployment.download_model_package_file to support downloading model package files (.mlpkg) of the currently deployed model.

• Added support for retrieving document thumbnails:

• DocumentThumbnail <datarobot.models.documentai.document.DocumentThumbnail>

• DocumentPageFile <datarobot.models.documentai.document.DocumentPageFile>

• Added support to retrieve document text extraction samples using:

• DocumentTextExtractionSample
• DocumentTextExtractionSamplePage

• DocumentTextExtractionSampleDocument

• Added new fields to CustomTaskVersion for controlling network policies. The new fields were also added to the response. This can be set with datarobot.enums.CustomTaskOutgoingNetworkPolicy.

• Added a new method BatchPredictionJob.score_with_leaderboard_model to run batch predictions using a Leaderboard model instead of a deployment.

• Set :IntakeSettings and OutputSettings to use IntakeAdapters and OutputAdapters enum values respectively for the property type.

• Added the method Deployment.get_predictions_vs_actuals_over_time to retrieve a deployment's predictions vs actuals over time data.

Bugfixes¶

• Payload property subset renamed to source in Model.request_feature_effect
• Fixed an issue where Context.trace_context was not being set from environment variables or DR config files.
• Project.refresh no longer sets Project.advanced_options to a dictionary.
• Fixed Dataset.modify to clarify behavior of when to preserve or clear categories.
• Fixed an issue with enums in f-strings resulting in the enum class and property being printed instead of the enum property's value in Python 3.11 environments.

Deprecation Summary¶

• Project.refresh will no longer set Project.advanced_options to a dictionary after version 3.5 is released. All interactions with Project.advanced_options should be expected to be through the AdvancedOptions class.

Experimental changes¶

• Added a new class, VectorDatabase, for interacting with DataRobot vector databases.
• VectorDatabase.get retrieves a VectorDatabase object by ID.
• VectorDatabase.list lists all VectorDatabases available to the user.
• VectorDatabase.create creates a new VectorDatabase.
• VectorDatabase.create allows you to use a validated deployment of a custom model as your own Vector Database.
• VectorDatabase.update updates the name of a VectorDatabase.
• VectorDatabase.delete deletes a single VectorDatabase.
• VectorDatabase.get_supported_embeddings retrieves all supported embedding models.
• VectorDatabase.get_supported_text_chunkings retrieves all supported text chunking configurations.

• VectorDatabase.download_text_and_embeddings_asset download a parquet file with internal vector database data.

• Added a new class, CustomModelVectorDatabaseValidation, for validating custom model deployments for use as a vector database.

• CustomModelVectorDatabaseValidation.get retrieves a CustomModelVectorDatabaseValidation object by ID.
• CustomModelVectorDatabaseValidation.get_by_values retrieves a CustomModelVectorDatabaseValidation object by field values.
• CustomModelVectorDatabaseValidation.create starts validation of the deployment.

• CustomModelVectorDatabaseValidation.revalidate repairs an unlinked external vector database.

• Added a new class, Playground, for interacting with DataRobot generative AI playgrounds.

• Playground.get retrieves a playground object by ID.
• Playground.list lists all playgrounds available to the user.
• Playground.create creates a new playground.
• Playground.update updates the name and description of a playground.

• Playground.delete deletes a single playground.

• Added a new class, LLMDefinition, for interacting with DataRobot generative AI LLMs.

• LLMDefinition.list lists all LLMs available to the user.

• Added a new class, LLMBlueprint, for interacting with DataRobot generative AI LLM blueprints.

• LLMBlueprint.get retrieves an LLM blueprint object by ID.
• LLMBlueprint.list lists all LLM blueprints available to the user.
• LLMBlueprint.create creates a new LLM blueprint.
• LLMBlueprint.create_from_llm_blueprint creates a new LLM blueprint from an existing one.
• LLMBlueprint.update updates an LLM blueprint.

• LLMBlueprint.delete deletes a single LLM blueprint.

• Added a new class, ChatPrompt, for interacting with DataRobot generative AI chat prompts.

• ChatPrompt.get retrieves a chat prompt object by ID.
• ChatPrompt.list lists all chat prompts available to the user.
• ChatPrompt.create creates a new chat prompt.

• ChatPrompt.delete deletes a single chat prompt.

• Added a new class, CustomModelLLMValidation, for validating custom model deployments for use as a custom model LLM.

• CustomModelLLMValidation.get retrieves a CustomModelLLMValidation object by ID.
• CustomModelLLMValidation.get_by_values retrieves a CustomModelLLMValidation object by field values.
• CustomModelLLMValidation.create starts validation of the deployment.

• CustomModelLLMValidation.revalidate repairs an unlinked external custom model LLM.

• Added a new class, ComparisonPrompt, for interacting with DataRobot generative AI comparison prompts.

• ComparisonPrompt.get retrieves a comparison prompt object by ID.
• ComparisonPrompt.list lists all comparison prompts available to the user.
• ComparisonPrompt.create creates a new comparison prompt.
• ComparisonPrompt.update updates a comparison prompt.

• ComparisonPrompt.delete deletes a single comparison prompt.

• Extended UseCase, adding two new fields to represent the count of vector databases and playgrounds.

• Added a new method, ChatPrompt.create_llm_blueprint, to create an LLM blueprint from a chat prompt.

• Added a new method, CustomModelLLMValidation.delete, to delete a custom model LLM validation record.

• Added a new method, LLMBlueprint.register_custom_model, for registering a custom model from a generative AI LLM blueprint.