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  • Generative Syftr search.
    • syftr is an optimizer that helps to find the best LLM blueprints for your data.
      • SearchStudy
        • SearchStudy.create()
        • SearchStudy.get()
        • SearchStudy.list()
        • SearchStudy.delete()
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DataRobot Python Client
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  • Generative Syftr search.

Generative Syftr search.

syftr is an optimizer that helps to find the best LLM blueprints for your data.

class datarobot.models.genai.syftr_search.SearchStudy

Metadata for a DataRobot syftr search study.

Parameters:

  • search_space (Optional[Dict[str, Any]]) – Search space configuration used for the study.

  • use_case_id (str) – The ID of the use case the search study is linked to.

  • grounding_dataset_id (str) – The ID of the dataset used to build vector databases.

  • eval_dataset_id (str) – The ID of the evaluation dataset.

  • grounding_dataset_name (str) – The name of the grounding dataset.

  • eval_dataset_name (str) – The name of the evaluation dataset.

  • user_id (str) – The ID of the user.

  • user_name (str) – The name of the user who ran the study.

  • num_trials (int) – The number of search trials to sample.

  • num_concurrent_trials (int) – The number of simultaneously running trials.

  • optimization_objectives (List[Tuple[str, str]]) – Optimization objectives of the study, defined as (objective, direction) pairs.

  • playground_id (str) – The ID of the associated playground.

  • temp_playground_id (Optional[str]) – The ID of the temporary playground.

  • pareto_front (Optional[List[Dict[str, Any]]]) – Pareto frontier of the study.

  • datetime_start (str) – Study start time.

  • datetime_end (Optional[str]) – Study end time.

  • study_status (str) – Status of the study (e.g., RUNNING, COMPLETED, FAILED).

  • search_study_id (str) – The ID of the search study.

  • name (str) – Name of the search study.

  • job_id (Optional[str]) – The ID of the worker job (UUID4).

  • trials_running (Optional[int]) – Number of currently running trials.

  • trials_failed (Optional[int]) – Number of failed trials.

  • trials_success (Optional[int]) – Number of completed trials.

  • all_trials (Optional[List[Dict[str, Any]]]) – Trials history.

  • existing_blueprint_ids (Optional[List[str]]) – IDs of existing LLM blueprints for comparative evaluation.

  • eval_results (Optional[List[Any]]) – Results of the comparative evaluation.

  • error_message (Optional[str]) – Error message if the study fails.

classmethod create(use_case_id, playground_id, grounding_dataset_id, eval_dataset_id, num_trials, num_concurrent_trials, optimization_objectives, search_space, name)

Create a new search search study with the specified parameters.

Parameters:

  • use_case_id (str) – The ID of the use case the search study is linked to.

  • playground_id (str) – The ID of the existing playground associated with the search.

  • grounding_dataset_id (str) – The ID of the dataset used to build vector databases.

  • eval_dataset_id (str) – The ID of the evaluation dataset.

  • num_trials (int) – The number of search trials to sample.

  • num_concurrent_trials (int) – The number of simultaneously running trials.

  • optimization_objectives (List[Tuple[ObjectiveType, DirectionType]]) – Optimization objectives of the study, defined as (objective, direction) pairs.

  • search_space (SearchSpaceDict) – Search space configuration for the search.

Returns:

search study – The created search study.

Return type:

SearchStudy

classmethod get(search_study_id)

Read an existing search study.

Parameters:

search_study_id (str) – ID of the search study used for creation.

Returns:

search study – The created search study database.

Return type:

SearchStudy

classmethod list(use_case, playground=None, offset=0, limit=200, search=None, sort=None)

List all syftr search studies associated with a specific use case available to the user.

Parameters:

  • use_case (UseCaseLike) – The returned search studies are filtered to those associated with a specific Use Case(s) if specified or can be inferred from the context. Accepts either the entity or the ID.

  • playground (Optional[Union[Playground, str]], optional) – The returned search studies are filtered to those associated with a specific playground if it is specified. Accepts either the entity or the ID.

  • search (Optional[str]) – String for filtering search studies. Search studies that contain the string in name will be returned. If not specified, all search studies will be returned.

  • sort (Optional[str]) – Property to sort search studies by. Prefix the attribute name with a dash to sort in descending order, e.g., sort=’-creationDate’. Currently supported options are “name”.

Returns:

search studies – A list of search studies available to the user.

Return type:

list[SearchStudy]

delete()

Delete the search study and all its related artifacts.

Return type:

None

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