DeepchecksLLMClient.get_data#
- DeepchecksLLMClient.get_data(app_name: str, version_name: str, environment: EnvType | str, return_topics: bool = True, return_annotation_data: bool = True, return_input_props: bool = True, return_output_props: bool = True, return_custom_props: bool = True, return_llm_props: bool = True, start_time: datetime | int | None = None, end_time: datetime | int | None = None, return_similarities: bool = False, user_interaction_ids: List[str] | None = None) DataFrame | None #
Fetch all the interactions from the specified environment (PROD/EVAL) as a pandas DataFrame.
- Parameters:
- app_namestr
Application name
- version_namestr
Name of application version
- environmentEnvType | str
The environment type from which to fetch interactions. This can be either “PROD” or “EVAL”.
- return_annotation_databool, optional
Whether to include annotation info in the data.
- return_topicsbool, optional
Whether to include the topic in the data.
- return_input_propsbool, optional
Whether to include input properties in the data.
- return_output_propsbool, optional
Whether to include the output properties in the data.
- return_custom_propsbool, optional
Whether to include custom properties in the data.
- return_llm_propsbool, optional
Whether to include LLM properties in the data.
- return_similaritiesbool, optional
Whether to include similarities in the data.
- start_timedatetime | int | None, optional
The start time from which to fetch interactions. This can be a datetime object or an integer. If not provided, interactions will be fetched from the beginning.
- end_timedatetime | int | None, optional
The end time until which to fetch interactions. This can be a datetime object or an integer. If not provided, interactions will be fetched up to the most recent.
- user_interaction_ids: list | None, optional
user interactions ids to include in the data.
- Returns:
- pd.DataFrame | None
A pandas DataFrame containing the interactions, or None in case of a problem retrieving the data.