Dataset

Object-centric interface for Splunk AO datasets. This class provides an intuitive way to work with Splunk AO datasets, encapsulating dataset management operations and providing seamless integration with dataset content management. Examples

Create a new dataset locally, then persist

dataset = Dataset( name=“ml-knowledge-evaluation_3”, content=[ {“input”: “What is machine learning?”, “output”: “Machine learning …”}, {“input”: “How does deep learning work?”, “output”: “Deep learning uses …”} ] ).create()

Get an existing dataset

dataset = Dataset.get(name=“geography-questions”)

List all datasets

datasets = Dataset.list(limit=50)

Get dataset content

content = dataset.get_content()

Add rows to dataset

dataset.add_rows([ {“input”: “Australia”, “output”: “Oceania”}, {“input”: “Egypt”, “output”: “Africa”}, ])

Get dataset versions

versions = dataset.get_versions()

Delete dataset

dataset.delete()

add_rows

def add_rows(self, rows: list[dict[str, Any]]) -> Dataset

Add rows to this dataset (active mutation). This method performs an API call and atomically updates the state. Arguments

rows (list[dict[str, Any]]): The rows to add to the dataset.

create

def create(self) -> Dataset

Persist this dataset to the API. Examples dataset = Dataset(name=“test”, content=[…]).create() assert dataset.is_synced()

delete

def delete(self) -> None

Delete this dataset. Examples dataset = Dataset.get(name=“my-dataset”) dataset.delete()

extend

def extend(self,
           *,
           prompt: str | None=None,
           instructions: str | None=None,
           examples: list[str] | None=None,
           count: int=10,
           data_types: list[str] | None=None,
           prompt_settings: dict[str, Any] | None=None) -> list[DatasetRow]

Extend this dataset with synthetically generated data. Arguments

prompt (Optional[str]): A description of the assistant’s role.
instructions (Optional[str]): Instructions for the assistant.
examples (Optional[list[str]]): Examples of user prompts.
count (int): The number of synthetic examples to generate.
data_types (Optional[list[str]]): The types of data to generate.
prompt_settings (Optional[dict[str, Any]]): Settings for the prompt generation.

generate

def generate(cls,
             *,
             prompt: str | None=None,
             instructions: str | None=None,
             examples: list[str] | None=None,
             count: int=10,
             data_types: list[str] | None=None,
             prompt_settings: dict[str, Any] | None=None) -> list[DatasetRow]

Generate synthetic dataset rows. Arguments

prompt (Optional[str]): A description of the assistant’s role.
instructions (Optional[str]): Instructions for the assistant.
examples (Optional[list[str]]): Examples of user prompts.
count (int): The number of synthetic examples to generate.
data_types (Optional[list[str]]): The types of data to generate.
prompt_settings (Optional[dict[str, Any]]): Settings for the prompt generation.

get

def get(cls, *, id: str | None=None, name: str | None=None) -> Dataset | None

Get an existing dataset by ID or name. Arguments

id (Optional[str]): The dataset ID.
name (Optional[str]): The dataset name.

get_content

def get_content(self) -> DatasetContent | None

Get the content of this dataset. Examples dataset = Dataset.get(name=“my-dataset”) content = dataset.get_content()

get_version_content

def get_version_content(self, *, index: int) -> DatasetVersionContent

Get the content of a specific version of this dataset. Arguments

index (int): The 1-based version index to retrieve. Must be >= 1.

get_versions

def get_versions(self) -> ListDatasetVersionResponse

Get a list of versions for this dataset. Examples dataset = Dataset.get(name=“my-dataset”) versions = dataset.get_versions() for v in versions.versions: print(v.version_index, v.num_rows)

list

def list(cls,
         *,
         limit: Unset | int=100,
         project_id: str | None=None,
         project_name: str | None=None) -> list[Dataset]

List all available datasets, optionally filtered by project. Arguments

limit (Union[Unset, int]): Maximum number of datasets to return.
project_id (Optional[str]): Filter datasets used in this project by ID.
project_name (Optional[str]): Filter datasets used in this project by name.

refresh

def refresh(self) -> None

Refresh this dataset’s state from the API. Updates all attributes with the latest values from the remote API and sets the state to SYNCED. Examples dataset.refresh() assert dataset.is_synced()

save

def save(self) -> Dataset

Save changes to this dataset. Persists any local changes (name) to the remote API. If the dataset is LOCAL_ONLY, delegates to create(). If SYNCED, returns immediately as a no-op. Raises ValueError for DELETED or FAILED_SYNC states. Examples dataset = Dataset.get(name=“my-dataset”) dataset.name = “renamed-dataset” dataset.save() assert dataset.is_synced()

Overview

Python SDK

API

dataset

Dataset

Create a new dataset locally, then persist

Get an existing dataset

List all datasets

Get dataset content

Add rows to dataset

Get dataset versions

Delete dataset

add_rows

create

delete

extend

generate

get

get_content

get_version_content

get_versions

list

refresh

save

​Dataset

​Create a new dataset locally, then persist

​Get an existing dataset

​List all datasets

​Get dataset content

​Add rows to dataset

​Get dataset versions

​Delete dataset

​add_rows

​create

​delete

​extend

​generate

​get

​get_content

​get_version_content

​get_versions

​list

​refresh

​save

Dataset

Create a new dataset locally, then persist

Get an existing dataset

List all datasets

Get dataset content

Add rows to dataset

Get dataset versions

Delete dataset

add_rows

create

delete

extend

generate

get

get_content

get_version_content

get_versions

list

refresh

save